In this paper, we present a neural network architecture that belongs to the multi-layer perceptron family, associated with two different algorithms: the ordinary gradient and the natural gradient, we compare performances of those algorithms. The identification of a non-normalized power amplifier yielded to the introduction of an additional weight in the classical multilayer perceptron structure...

The water quality of the Karaj River was studied through collecting 2137 experimental data set gained by 20 sampling stations. The data included different parameters such as T (temperature), pH, NTU (turbidity), hardness, TDS (total dissolved solids), EC (electrical conductivity) and basic anion, cation concentrations. In this study a multi-layer perceptron artificial neural network model was d...

This report examines the fault tolerance of multi-layer perceptron networks. First, the operation of a single perceptron unit is analysed, and it is found that they are highly fault tolerant. This suggests that neural networks composed from these units could in theory be extremely reliable. The multi-layer perceptron network was then examined, but surprisingly was found to be non-fault tolerant...

Speaker Recognition is a challenging task and is widely used in many speech aided applications. This study proposes a new Neural Network (NN) model for identifying the speaker, based on the acoustic features of a given speech sample extracted by applying wavelet transform on raw signals. Wrapper based feature selection applies dimensionality reduction by kernel PCA and ranking by Info gain. Onl...

In this paper, the application of neural networks to study the maximum and minimum relative humidity for Chandigarh city is explored. One important architecture of neural networks named Multi-Layer Perceptron (MLP) to model forecasting system is used and Back Propagation algorithm is used to train the network. The proposed network is trained with actual data of the past 10 years (2000-2010) and...

Advancement in Artificial Intelligence has lead to the developments of various “smart” devices. The task of face Recognition has been actively researched in recent years. Wide usage of biometric information for person identity verification purposes, terrorist acts prevention measures and authentication process simplification in computer systems has raised significant attention to reliability an...

In this paper a new two-layer linear-in-the-parameters feedforward network termed the Functionally Expanded Neural Network (FENN) is presented, together with its design strategy and learning algorithm. It is essentially a hybrid neural network incorporating a variety of non-linear basis functions within its single hidden layer which emulate other universal approximators employed in the conventi...

-This paper presents a mathematical analysis of the occurrence of temporary minima during training of a single-output, two-layer neural network, with learning according to the back-propagation algorithm. A new vector decomposition method is introduced, which simplifies the mathematical analysis of learning of neural networks considerably. The analysis shows that temporary minima are inherent to...

in this paper, genetic programming is applied for quality improvement of noisy speech signal. therefore, a system including both spectral subtraction and genetic programming is implemented for speech enhancement. in the proposed method, first noise is reduced by spectral subtraction. in the next step, genetic programming trees are trained for more enhancement of noisy signal by mapping the sign...