Search results for: protein structure prediction

Authors: A. Farshidianfar, P. Oliazadeh

Abstract:

In this paper the vibration behaviors of a structure equipped with a tuned liquid column damper (TLCD) under a harmonic type of earthquake loading are studied. However, due to inherent nonlinear liquid damping, it is no doubt that a great deal of computational effort is required to search the optimum parameters of the TLCD, numerically. Therefore by linearization the equation of motion of the single degree of freedom structure equipped with the TLCD, the closed form solutions of the TLCD-structure system are derived. To find the reliability of the analytical method, the results have been compared with other researcher and have good agreement. Further, the effects of optimal design parameters such as length ratio and mass ratio on the performance of the TLCD for controlling the responses of a structure are investigated by using the harmonic type of earthquake excitation. Finally, the Citicorp Center which has a very flexible structure is used as an example to illustrate the design procedure for the TLCD under the earthquake excitation.

Keywords: Closed form solution, Earthquake excitation, TLCD.

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Authors: Antoni Wibowo, Harry Pujianto, Dewi Retno Sari Saputro

Abstract:

The stock market can provide huge profits in a relatively short time in financial sector; however, it also has a high risk for investors and traders if they are not careful to look the factors that affect the stock market. Therefore, they should give attention to the dynamic fluctuations and movements of the stock market to optimize profits from their investment. In this paper, we present a nonlinear autoregressive exogenous model (NARX) to predict the movements of stock market; especially, the movements of the closing price index. As case study, we consider to predict the movement of the closing price in Indonesia composite index (IHSG) and choose the best structures of NARX for IHSG’s prediction.

Keywords: NARX, prediction, stock market, time series.

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Authors: Amiel Satvedi, Richard Pyne

Abstract:

Injuries in professional sports occur on a regular basis. Some may be minor while others can cause huge impact on a player’s career and earning potential. In soccer, there is a high risk of players picking up injuries during game time. This research work seeks to help soccer players reduce the risk of getting injured by predicting the likelihood of injury while playing in the near future and then providing recommendations for intervention. The injury prediction tool will use a soccer player’s number of minutes played on the field, number of appearances, distance covered and performance data for the current and previous seasons as variables to conduct statistical analysis and provide injury predictive results using a machine learning linear regression model.

Keywords: Injury predictor, soccer injury prevention, machine learning in soccer, big data in soccer.

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Authors: A. H. Akhaveissy

Abstract:

Nonlinear finite element method with eight noded isoparametric quadrilateral element is used for prediction of loaddeformation behavior including bearing capacity of foundations. Modified generalized plasticity model with non-associated flow rule is applied for analysis of soil-footing system. Also Von Mises and Tresca criterions are used for simulation of soil behavior. Modified generalized plasticity model is able to simulate load-deformation including softening behavior. Localization phenomena are considered by different meshes. Localization phenomena have not been seen in the examples. Predictions by modified generalized plasticity model show good agreement with laboratory data and theoretical prediction in comparison the other models.

Keywords: Localization phenomena, Generalized plasticity, Non-associated Flow Rule

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Authors: Fatih Kagnici

Abstract:

Improvement in CAE methods has an important role for shortening of the vehicle product development time. It is provided that validation of the design and improvements in terms of durability can be done without hardware prototype production. In recent years, several different methods have been developed in order to investigate fatigue damage of the vehicle. The intended goal among these methods is prediction of fatigue damage in a short time with reduced costs. This study developed a new fatigue damage prediction method in the automotive sector using power spectrum densities of accelerations. This study also confirmed that the weak region in vehicle can be easily detected with the method developed in this study which results were compared with conventional method.

Keywords: Fatigue damage, Power spectrum density, Vibration induced fatigue, Vehicle development

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Authors: İsmail Hakkı Kalyoncu, Nilda Ersoy, Ayşe Yalcın Elidemir, Muhammed Emin Karalı

Abstract:

Gilaburu (Viburnum opulus L.) grown naturally in Anatolia. In this study, some physico-chemical (sugar, acid, protein, crude fat, crude fiber, ash etc.) characteristics and mineral composition of Gilaburu fruit have been investigated. The length, width, thickness, weight, total soluble solid, protein, crude ash, crude fiber and crude oil of fruit were found to be 1.12 cm, 1.58 cm, 1.87 cm, 0.87 g, 14.73 %, 0.2 %, 0.11 %, 6.56 % and 0.4 %, respectively. The seed of fruit mean weight, length, width and thickness were determinated as 0.08 g, 7.76 cm, 7.67 cm and 1.66, respectively. In addition 27 mineral elements (Al, Mg, Na, Ba, Ca, Ni, Cd, P, Cr, Pb, S, Cu, Se, Fe, K, Sr, Li, Z, V, Ag, Bi, Co, Mn, B, Ga, In, Ti) were analyzed. Gilaburu (Viburnum opulus L.) fruit was richest in potassium (10764.764 ppm), Mg (1289.088 ppm) and P (1304.169 ppm).

Keywords: Gilaburu (Viburnum opulus L.), nutritional properties, physico-chemical properties.

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Authors: A. R. Azrin Hani, R. Ahmad, M. Mariatti

Abstract:

Textile structures are engineered and fabricated to meet worldwide structural applications. Nevertheless, research varying textile structure on natural fibre as composite reinforcement was found to be very limited. Most of the research is focusing on short fibre and random discontinuous orientation of the reinforcement structure. Realizing that natural fibre (NF) composite had been widely developed to be used as synthetic fibre composite replacement, this research attempted to examine the influence of woven and cross-ply laminated structure towards its mechanical performances. Laminated natural fibre composites were developed using hand lay-up and vacuum bagging technique. Impact and flexural strength were investigated as a function of fibre type (coir and kenaf) and reinforcement structure (imbalanced plain woven, 0°/90° cross-ply and +45°/-45° cross-ply). Multi-level full factorial design of experiment (DOE) and analysis of variance (ANOVA) was employed to impart data as to how fibre type and reinforcement structure parameters affect the mechanical properties of the composites. This systematic experimentation has led to determination of significant factors that predominant influences the impact and flexural properties of the textile composites. It was proven that both fibre type and reinforcement structure demonstrated significant difference results. Overall results indicated that coir composite and woven structure exhibited better impact and flexural strength. Yet, cross-ply composite structure demonstrated better fracture resistance.

Keywords: Cross-ply composite, Flexural strength, Impact strength, Textile natural fibre composite, Woven composite.

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Authors: Minky Son, Chanin Park, Ayoung Baek, Shalini John, Keun Woo Lee

Abstract:

3-hydroxy-3-methylglutaryl coenzyme A reductase (HMGR) catalyzes the conversion of HMG-CoA to mevalonate using NADPH and the enzyme is involved in rate-controlling step of mevalonate. Inhibition of HMGR is considered as effective way to lower cholesterol levels so it is drug target to treat hypercholesterolemia, major risk factor of cardiovascular disease. To discover novel HMGR inhibitor, we performed structure-based pharmacophore modeling combined with molecular dynamics (MD) simulation. Four HMGR inhibitors were used for MD simulation and representative structure of each simulation were selected by clustering analysis. Four structure-based pharmacophore models were generated using the representative structure. The generated models were validated used in virtual screening to find novel scaffolds for inhibiting HMGR. The screened compounds were filtered by applying drug-like properties and used in molecular docking. Finally, four hit compounds were obtained and these complexes were refined using energy minimization. These compounds might be potential leads to design novel HMGR inhibitor.

Keywords: Anti-hypercholesterolemia drug, HMGR inhibitor, Molecular dynamics simulation, Structure-based pharmacophore modeling.

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Authors: Danilo López, Johana Hernández, Edwin Rivas

Abstract:

The use of the Multilayer Perceptron Neural Networks (MLPNN) technique is presented to estimate the future state of use of a licensed channel by primary users (PUs); this will be useful at the spectral decision stage in cognitive radio networks (CRN) to determine approximately in which time instants of future may secondary users (SUs) opportunistically use the spectral bandwidth to send data through the primary wireless network. To validate the results, sequences of occupancy data of channel were generated by simulation. The results show that the prediction percentage is greater than 60% in some of the tests carried out.

Keywords: Cognitive radio, neural network, prediction, primary user.

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Authors: Eunji Choi, Borang Park, Youngjae Choi, Jinwoo Moon

Abstract:

In this study, a predictive model for estimating the metabolism (MET) of human body was developed for the optimal control of indoor thermal environment. Human body images for indoor activities and human body joint coordinated values were collected as data sets, which are used in predictive model. A deep learning algorithm was used in an initial model, and its number of hidden layers and hidden neurons were optimized. Lastly, the model prediction performance was analyzed after the model being trained through collected data. In conclusion, the possibility of MET prediction was confirmed, and the direction of the future study was proposed as developing various data and the predictive model.

Keywords: Deep learning, indoor quality, metabolism, predictive model.

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Authors: S.I Sulaiman, T.K Abdul Rahman, I. Musirin, S. Shaari

Abstract:

This paper presents performance analysis of the Evolutionary Programming-Artificial Neural Network (EPANN) based technique to optimize the architecture and training parameters of a one-hidden layer feedforward ANN model for the prediction of energy output from a grid connected photovoltaic system. The ANN utilizes solar radiation and ambient temperature as its inputs while the output is the total watt-hour energy produced from the grid-connected PV system. EP is used to optimize the regression performance of the ANN model by determining the optimum values for the number of nodes in the hidden layer as well as the optimal momentum rate and learning rate for the training. The EPANN model is tested using two types of transfer function for the hidden layer, namely the tangent sigmoid and logarithmic sigmoid. The best transfer function, neural topology and learning parameters were selected based on the highest regression performance obtained during the ANN training and testing process. It is observed that the best transfer function configuration for the prediction model is [logarithmic sigmoid, purely linear].

Keywords: Artificial neural network (ANN), Correlation coefficient (R), Evolutionary programming-ANN (EPANN), Photovoltaic (PV), logarithmic sigmoid and tangent sigmoid.

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Authors: Shreya Thusoo, Karan Modi, Rajesh Kumar, Hitesh Madahar

Abstract:

Over the years, it has been extensively established that the practice of assuming a structure being fixed at base, leads to gross errors in evaluation of its overall response due to dynamic loadings and overestimations in design. The extent of these errors depends on a number of variables; soil type being one of the major factor. This paper studies the effect of Soil Structure Interaction (SSI) on multistorey buildings with varying under-laying soil types after proper validation of the effect of SSI. Analysis for soft, stiff and very stiff base soils has been carried out, using a powerful Finite Element Method (FEM) software package ANSYS v14.5. Results lead to some very important conclusions regarding time period, deflection and acceleration responses.

Keywords: Dynamic response, multi-storey building, Soil-Structure Interaction.

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Authors: Buntara Sthenly Gan

Abstract:

A novel and versatile numerical technique to solve a self-stress equilibrium state is adopted herein as a form-finding procedure for an irregular tensegrity structure. The numerical form-finding scheme of a tensegrity structure uses only the connectivity matrix and prototype tension coefficient vector as the initial guess solution. Any information on the symmetrical geometry or other predefined initial structural conditions is not necessary to get the solution in the form-finding process. An eight-node initial condition example is presented to demonstrate the efficiency and robustness of the proposed method in the form-finding of an irregular tensegrity structure. Based on the conception from the form-finding of an eight-node irregular tensegrity structure, a monumental object is designed by considering the real world situation such as self-weight, wind and earthquake loadings.

Keywords: Tensegrity, Form-finding, Design, Irregular, Self-stress, Force density method.

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Authors: H.Mohammadi Majd, M.Jalali Azizpour, A.V. Hoseini

Abstract:

In this paper back-propagation artificial neural network (BPANN) is employed to predict the limiting drawing ratio (LDR) of the deep drawing process. To prepare a training set for BPANN, some finite element simulations were carried out. die and punch radius, die arc radius, friction coefficient, thickness, yield strength of sheet and strain hardening exponent were used as the input data and the LDR as the specified output used in the training of neural network. As a result of the specified parameters, the program will be able to estimate the LDR for any new given condition. Comparing FEM and BPANN results, an acceptable correlation was found.

Keywords: Back-propagation artificial neural network(BPANN), deep drawing, prediction, limiting drawing ratio (LDR).

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Authors: Jan Stodt, Christoph Reich

Abstract:

The usage of machine learning models for prediction is growing rapidly and proof that the intended requirements are met is essential. Audits are a proven method to determine whether requirements or guidelines are met. However, machine learning models have intrinsic characteristics, such as the quality of training data, that make it difficult to demonstrate the required behavior and make audits more challenging. This paper describes an ML audit framework that evaluates and reviews the risks of machine learning applications, the quality of the training data, and the machine learning model. We evaluate and demonstrate the functionality of the proposed framework by auditing an steel plate fault prediction model.

Keywords: Audit, machine learning, assessment, metrics.

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Authors: Zane Turner, Kevin Labille, Susan Gauch

Abstract:

Sentiment analysis is a broad and expanding field that aims to extract and classify opinions from textual data. Lexicon-based approaches are based on the use of a sentiment lexicon, i.e., a list of words each mapped to a sentiment score, to rate the sentiment of a text chunk. Our work focuses on predicting stock price change using a sentiment lexicon built from financial conference call logs. We present a method to generate a sentiment lexicon based upon an existing probabilistic approach. By using a domain-specific lexicon, we outperform traditional techniques and demonstrate that domain-specific sentiment lexicons provide higher accuracy than generic sentiment lexicons when predicting stock price change.

Keywords: Lexicon, sentiment analysis, stock movement prediction., computational finance.

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Authors: Homa Torabizadeh, Mohaddeseh Mikani

Abstract:

Inulinase from Aspergillus niger was covalently immobilized on magnetic nanoparticles (MNPs/Fe₃O₄) covered with soy protein isolate (SPI/Fe₃O₄) functionalized by bovine serum albumin (BSA) nanoparticles. MNPs are promising enzyme carriers because they separate easily under external magnetic fields and have enhanced immobilized enzyme reusability. As MNPs aggregate simply, surface coating strategy was employed. SPI functionalized by BSA was a suitable candidate for nanomagnetite coating due to its superior biocompatibility and hydrophilicity. Fe₃O₄@SPI-BSA nanoparticles were synthesized as a novel carrier with narrow particle size distribution. Step by step fabrication monitoring of Fe₃O₄@SPI-BSA nanoparticles was performed using field emission scanning electron microscopy and dynamic light scattering. The results illustrated that nanomagnetite with the spherical morphology was well monodispersed with the diameter of about 35 nm. The average size of the SPI-BSA nanoparticles was 80 to 90 nm, and their zeta potential was around −34 mV. Finally, the mean diameter of fabricated Fe₃O₄@SPI-BSA NPs was less than 120 nm. Inulinase enzyme from Aspergillus niger was covalently immobilized through gluteraldehyde on Fe₃O₄@SPI-BSA nanoparticles successfully. Fourier transform infrared spectra and field emission scanning electron microscopy images provided sufficient proof for the enzyme immobilization on the nanoparticles with 80% enzyme loading.

Keywords: High fructose syrup, inulinase immobilization, functionalized magnetic nanoparticles, soy protein isolate.

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Authors: J. Hodicky, P. Frantis

Abstract:

Command and Control (C2) system and its interfacethe Common Operational Picture (COP) are main means that supports commander in its decision making process. COP contains information about friendly and enemy unit positions. The friendly position is gathered via tactical network. In the case of tactical network failure the information about units are not available. The tactical simulator can be used as a tool that is capable to predict movements of units in respect of terrain features. Article deals with an experiment that was based on Czech C2 system that is in the case of connectivity lost fed by VR Forces simulator. Article analyzes maximum time interval in which the position created by simulator is still usable and truthful for commander in real time.

Keywords: command and control system, movement prediction, simulation

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Authors: Zane Turner, Kevin Labille, Susan Gauch

Abstract:

Sentiment analysis is a broad and expanding field that aims to extract and classify opinions from textual data. Lexicon-based approaches are based on the use of a sentiment lexicon, i.e., a list of words each mapped to a sentiment score, to rate the sentiment of a text chunk. Our work focuses on predicting stock price change using a sentiment lexicon built from financial conference call logs. We introduce a method to generate a sentiment lexicon based upon an existing probabilistic approach. By using a domain-specific lexicon, we outperform traditional techniques and demonstrate that domain-specific sentiment lexicons provide higher accuracy than generic sentiment lexicons when predicting stock price change.

Keywords: Computational finance, sentiment analysis, sentiment lexicon, stock movement prediction.

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Authors: H.Mohammadi Majd, M.Jalali Azizpour, M. Goodarzi

Abstract:

In this paper back-propagation artificial neural network (BPANN) with Levenberg–Marquardt algorithm is employed to predict the limiting drawing ratio (LDR) of the deep drawing process. To prepare a training set for BPANN, some finite element simulations were carried out. die and punch radius, die arc radius, friction coefficient, thickness, yield strength of sheet and strain hardening exponent were used as the input data and the LDR as the specified output used in the training of neural network. As a result of the specified parameters, the program will be able to estimate the LDR for any new given condition. Comparing FEM and BPANN results, an acceptable correlation was found.

Keywords: BPANN, deep drawing, prediction, limiting drawingratio (LDR), Levenberg–Marquardt algorithm

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Authors: Dorota Witkowska, Paweł Gosek, Lukasz Swiecicki, Wojciech Jernajczyk, Bruce J. West, Miroslaw Latka

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In clinical practice, the selection of an antidepressant often degrades to lengthy trial-and-error. In this work we employ a normalized wavelet power of alpha waves as a biomarker of antidepressant treatment response. This novel EEG metric takes into account both non-stationarity and intersubject variability of alpha waves. We recorded resting, 19-channel EEG (closed eyes) in 22 inpatients suffering from unipolar (UD, n=10) or bipolar (BD, n=12) depression. The EEG measurement was done at the end of the short washout period which followed previously unsuccessful pharmacotherapy. The normalized alpha wavelet power of 11 responders was markedly different than that of 11 nonresponders at several, mostly temporoparietal sites. Using the prediction of treatment response based on the normalized alpha wavelet power, we achieved 81.8% sensitivity and 81.8% specificity for channel T4.

Keywords: Alpha waves, antidepressant, treatment outcome, wavelet.

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Authors: A. Medjnoun, R. Bahar

Abstract:

The disorders caused by the shrinking-swelling phenomenon are prevalent in arid and semi-arid in the presence of swelling clay. This soil has the characteristic of changing state under the effect of water solicitation (wetting and drying). A set of geotechnical parameters is necessary for the characterization of this soil type, such as state parameters, physical and chemical parameters and mechanical parameters. Some of these tests are very long and some are very expensive, hence the use or methods of predictions. The complexity of this phenomenon and the difficulty of its characterization have prompted researchers to use several identification parameters in the prediction of swelling potential. This document is an analytical and statistical study of geotechnical parameters affecting the potential of swelling clays. This work is performing on a database obtained from investigations swelling Algerian soil. The obtained observations have helped us to understand the soil swelling structure and its behavior.

Keywords: Analysis, estimated model, parameter identification, Swelling of clay.

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Authors: Li Yongfeng, Liu Yixing, Li Jian, Li Jun

Abstract:

A novel biomass composite inspired from wood porous structure was manufactured by impregnating vinyl monomer into wood cellular structure under vacuum conditions, and initiating the monomer for in situ polymerization through a thermal treatment. The vacuum condition was studied, and the mechanical properties of the composite were also tested. SEM observation shows that polymer generated in the wood porous structure, and strongly interacted with wood matrix; and the polymer content increased with vacuum value increasing. FTIR indicates that polymer grafted onto wood matrix, resulting chemical complex between them. The rate of monomer loading increased with increasing vacuum value and time, accordance with rate of polymer loading. The compression strength and modulus of elasticity linearly increased with the increasing rate of polymer loading. Results indicate that the novel biomass composite possesses good mechanical properties capable of applying in the fields of construction, traffic and so forth.

Keywords: Biomass composite, manufacture, vinyl monomer, wood cellular structure.

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Authors: J.W. Xiang, M. Liang

Abstract:

This paper presents a method to detect multiple cracks based on frequency information. When a structure is subjected to dynamic or static loads, cracks may develop and the modal frequencies of the cracked structure may change. To detect cracks in a structure, we construct a high precision wavelet finite element (EF) model of a certain structure using the B-spline wavelet on the interval (BSWI). Cracks can be modeled by rotational springs and added to the FE model. The crack detection database will be obtained by solving that model. Then the crack locations and depths can be determined based on the frequency information from the database. The performance of the proposed method has been numerically verified by a rotor example.

Keywords: Rotor, frequency measurement, multiple cracks, wavelet finite element method, identification.

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Authors: Fatima Zohra Bissaad, Farid Bounaceur, Nassima Behidj, Nadjiba Chebouti, Fatma Halouane, Bahia Doumandji-Mitiche

Abstract:

Effect of Beauveria bassiana and Metarhizium anisopliae var. acridum on the 5^th instar nymphs of Schistocerca gregaria was studied in the laboratory. Infection by these both entomopathogenic fungi caused reduction in the hemolymph total protein. The average amounts of total proteins were 2.3, 2.07, 2.09 µg/100 ml of haemolymph in the control and M. anisopliae var. acridum, and B. bassiana based-treatments, respectively. Three types of haemocytes were recognized and identified as prohaemocytes, plasmatocytes and granulocytes. The treatment caused significant reduction in the total haemocyte count and in each haemocyte type on the 9^th day after its application.

Keywords: Beauveria bassiana, haemolymph picture, haemolymph protein, Metarhizium anisopliae var. acridum, Schistocerca gregaria.

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Authors: Haifeng Wang, Haili Zhang

Abstract:

Most movie recommendation systems have been developed for customers to find items of interest. This work introduces a predictive model usable by small and medium-sized enterprises (SMEs) who are in need of a data-based and analytical approach to stock proper movies for local audiences and retain more customers. We used classification models to extract features from thousands of customers’ demographic, behavioral and social information to predict their movie genre preference. In the implementation, a Gaussian kernel support vector machine (SVM) classification model and a logistic regression model were established to extract features from sample data and their test error-in-sample were compared. Comparison of error-out-sample was also made under different Vapnik–Chervonenkis (VC) dimensions in the machine learning algorithm to find and prevent overfitting. Gaussian kernel SVM prediction model can correctly predict movie genre preferences in 85% of positive cases. The accuracy of the algorithm increased to 93% with a smaller VC dimension and less overfitting. These findings advance our understanding of how to use machine learning approach to predict customers’ preferences with a small data set and design prediction tools for these enterprises.

Keywords: Computational social science, movie preference, machine learning, SVM.

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Authors: WooYoung Jung, Buntheng Chhorn, Min-Gi Kim

Abstract:

Recently, damage to domestic facilities by strong winds and typhoons are growing. Therefore, this study focused on sign structure among various vulnerable facilities. The evaluation of the wind fragility was carried out considering the destruction of the anchor, which is one of the various failure modes of the sign structure. The performance evaluation of the anchor was carried out to derive the wind fragility. Two parameters were set and four anchor types were selected to perform the pull-out and shear tests. The resistance capacity was estimated based on the experimental results. Wind loads were estimated using Monte Carlo simulation method. Based on these results, we derived the wind fragility according to anchor type and wind exposure category. Finally, the evaluation of the wind fragility was performed according to the experimental parameters such as anchor length and anchor diameter. This study shows that the depth of anchor was more significant for the safety of structure compare to diameter of anchor.

Keywords: Sign structure, wind fragility, set anchor, pull-out test, shear test, Monte Carlo simulation.

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Authors: Thevaneyan K. David, John P. Forth

Abstract:

Integral Abutment Bridges (IAB) are defined as simple or multiple span bridges in which the bridge deck is cast monolithically with the abutment walls. This kind of bridges are becoming very popular due to different aspects such as good response under seismic loading, low initial costs, elimination of bearings, and less maintenance. However the main issue related to the analysis of this type of structures is dealing with soil-structure interaction of the abutment walls and the supporting piles. Various soil constitutive models have been used in studies of soil-structure interaction in this kind of structures by researchers. This paper is an effort to review the implementation of various finite elements model which explicitly incorporates the nonlinear soil and linear structural response considering various soil constitutive models and finite element mesh.

Keywords: Constitutive Models, FEM, Integral AbutmentBridges, Soil-structure Interactions

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Authors: Ardeshir Karami Mohammadi

Abstract:

A Variable Structure Model Reference Adaptive Controller using state variables is proposed for a class of multi input-multi output systems. Adaptation law is of variable structure type and switching functions is designed based on stability requirements. Global exponential stability is proved based on Lyapunov criterion. Transient behavior is analyzed using sliding mode control and shows perfect model following at a finite time.

Keywords: Adaptive control, Model reference, Variablestructure, MIMO system.

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Authors: X. Z. Gao, S. J. Ovaska, X. Wang

Abstract:

Prediction of sinusoidal signals with time-varying frequencies has been an important research topic in power electronics systems. To solve this problem, we propose a new fuzzy predictive filtering scheme, which is based on a Finite Impulse Response (FIR) filter bank. Fuzzy logic is introduced here to provide appropriate interpolation of individual filter outputs. Therefore, instead of regular 'hard' switching, our method has the advantageous 'soft' switching among different filters. Simulation comparisons between the fuzzy predictive filtering and conventional filter bank-based approach are made to demonstrate that the new scheme can achieve an enhanced prediction performance for slowly changing sinusoidal input signals.

Keywords: Predictive filtering, fuzzy logic, sinusoidal signals, time-varying frequencies.

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