**Commenced**in January 2007

**Frequency:**Monthly

**Edition:**International

**Paper Count:**11

# Search results for: Back-propagation algorithm

##### 11 Modeling and Analysis of Concrete Slump Using Hybrid Artificial Neural Networks

**Authors:**
Vinay Chandwani,
Vinay Agrawal,
Ravindra Nagar

**Abstract:**

Artificial Neural Networks (ANN) trained using backpropagation (BP) algorithm are commonly used for modeling material behavior associated with non-linear, complex or unknown interactions among the material constituents. Despite multidisciplinary applications of back-propagation neural networks (BPNN), the BP algorithm possesses the inherent drawback of getting trapped in local minima and slowly converging to a global optimum. The paper present a hybrid artificial neural networks and genetic algorithm approach for modeling slump of ready mix concrete based on its design mix constituents. Genetic algorithms (GA) global search is employed for evolving the initial weights and biases for training of neural networks, which are further fine tuned using the BP algorithm. The study showed that, hybrid ANN-GA model provided consistent predictions in comparison to commonly used BPNN model. In comparison to BPNN model, the hybrid ANNGA model was able to reach the desired performance goal quickly. Apart from the modeling slump of ready mix concrete, the synaptic weights of neural networks were harnessed for analyzing the relative importance of concrete design mix constituents on the slump value. The sand and water constituents of the concrete design mix were found to exhibit maximum importance on the concrete slump value.

**Keywords:**
Artificial neural networks,
Genetic algorithms,
Back-propagation algorithm,
Ready Mix Concrete,
Slump value.

##### 10 A Critics Study of Neural Networks Applied to ion-Exchange Process

**Authors:**
John Kabuba,
Antoine Mulaba-Bafubiandi,
Kim Battle

**Abstract:**

**Keywords:**
Copper,
ion-exchange process,
neural networks,
simulation

##### 9 Application of Feed-Forward Neural Networks Autoregressive Models with Genetic Algorithm in Gross Domestic Product Prediction

**Authors:**
E. Giovanis

**Abstract:**

**Keywords:**
Autoregressive model,
Feed-Forward neuralnetworks,
Genetic Algorithms,
Gross Domestic Product

##### 8 Automatic Removal of Ocular Artifacts using JADE Algorithm and Neural Network

**Authors:**
V Krishnaveni,
S Jayaraman,
A Gunasekaran,
K Ramadoss

**Abstract:**

**Keywords:**
Auto Regressive (AR) Coefficients,
Feed Forward Neural Network (FNN),
Joint Approximation Diagonalisation of Eigen matrices (JADE) Algorithm,
Polynomial Neural Network (PNN).

##### 7 Complex-Valued Neural Network in Signal Processing: A Study on the Effectiveness of Complex Valued Generalized Mean Neuron Model

**Authors:**
Anupama Pande,
Ashok Kumar Thakur,
Swapnoneel Roy

**Abstract:**

**Keywords:**
Complex valued neural network,
Generalized Meanneuron model,
Signal processing.

##### 6 Pattern Classification of Back-Propagation Algorithm Using Exclusive Connecting Network

**Authors:**
Insung Jung,
Gi-Nam Wang

**Abstract:**

**Keywords:**
Neural network,
Back-propagation,
classification.

##### 5 Optimization of a Three-Term Backpropagation Algorithm Used for Neural Network Learning

**Authors:**
Yahya H. Zweiri

**Abstract:**

**Keywords:**
Neural Networks,
Backpropagation,
Optimization.

##### 4 Two States Mapping Based Neural Network Model for Decreasing of Prediction Residual Error

**Authors:**
Insung Jung,
lockjo Koo,
Gi-Nam Wang

**Abstract:**

The objective of this paper is to design a model of human vital sign prediction for decreasing prediction error by using two states mapping based time series neural network BP (back-propagation) model. Normally, lot of industries has been applying the neural network model by training them in a supervised manner with the error back-propagation algorithm for time series prediction systems. However, it still has a residual error between real value and prediction output. Therefore, we designed two states of neural network model for compensation of residual error which is possible to use in the prevention of sudden death and metabolic syndrome disease such as hypertension disease and obesity. We found that most of simulations cases were satisfied by the two states mapping based time series prediction model compared to normal BP. In particular, small sample size of times series were more accurate than the standard MLP model. We expect that this algorithm can be available to sudden death prevention and monitoring AGENT system in a ubiquitous homecare environment.

**Keywords:**
Neural network,
U-healthcare,
prediction,
timeseries,
computer aided prediction.

##### 3 A New Technique for Solar Activity Forecasting Using Recurrent Elman Networks

**Authors:**
Salvatore Marra,
Francesco C. Morabito

**Abstract:**

In this paper we present an efficient approach for the prediction of two sunspot-related time series, namely the Yearly Sunspot Number and the IR5 Index, that are commonly used for monitoring solar activity. The method is based on exploiting partially recurrent Elman networks and it can be divided into three main steps: the first one consists in a “de-rectification" of the time series under study in order to obtain a new time series whose appearance, similar to a sum of sinusoids, can be modelled by our neural networks much better than the original dataset. After that, we normalize the derectified data so that they have zero mean and unity standard deviation and, finally, train an Elman network with only one input, a recurrent hidden layer and one output using a back-propagation algorithm with variable learning rate and momentum. The achieved results have shown the efficiency of this approach that, although very simple, can perform better than most of the existing solar activity forecasting methods.

**Keywords:**
Elman neural networks,
sunspot,
solar activity,
time series prediction.

##### 2 Low Resolution Single Neural Network Based Face Recognition

**Authors:**
Jahan Zeb,
Muhammad Younus Javed,
Usman Qayyum

**Abstract:**

**Keywords:**
Average filtering,
Bicubic Interpolation,
Neurons,
vectorization.

##### 1 Complex-Valued Neural Network in Image Recognition: A Study on the Effectiveness of Radial Basis Function

**Authors:**
Anupama Pande,
Vishik Goel

**Abstract:**

A complex valued neural network is a neural network, which consists of complex valued input and/or weights and/or thresholds and/or activation functions. Complex-valued neural networks have been widening the scope of applications not only in electronics and informatics, but also in social systems. One of the most important applications of the complex valued neural network is in image and vision processing. In Neural networks, radial basis functions are often used for interpolation in multidimensional space. A Radial Basis function is a function, which has built into it a distance criterion with respect to a centre. Radial basis functions have often been applied in the area of neural networks where they may be used as a replacement for the sigmoid hidden layer transfer characteristic in multi-layer perceptron. This paper aims to present exhaustive results of using RBF units in a complex-valued neural network model that uses the back-propagation algorithm (called 'Complex-BP') for learning. Our experiments results demonstrate the effectiveness of a Radial basis function in a complex valued neural network in image recognition over a real valued neural network. We have studied and stated various observations like effect of learning rates, ranges of the initial weights randomly selected, error functions used and number of iterations for the convergence of error on a neural network model with RBF units. Some inherent properties of this complex back propagation algorithm are also studied and discussed.

**Keywords:**
Complex valued neural network,
Radial BasisFunction,
Image recognition.