A combination of improved genetic algorithm and neural networks is proposed to find the optimal weight of structures subject to multiple natural frequency constraints. The structural optimization is carried out by an evolutionary algorithm employing the discrete design variables. To reduce the computational time of the optimization process, the natural frequencies of structures are evaluated by...

Image compression technique is used to reduce the number of bits required in representing image, which helps to reduce the storage space and transmission cost. In the present research work back propagation neural network training algorithm has been used. Back propagation neural network algorithm helps to increase the performance of the system and to decrease the convergence time for the trainin...

The back propagation (BP) neural networks have been commonly used for automatic modulation recognition since the late 1990s. However, the back propagation algorithm easily falls into local minimum and the network learning is sensitive to initial weight values which usually determined by experience. The particle swarm optimization (PSO) algorithm is a global heuristic searching technology. By co...

In this article the potential of Radial Basis Function Neural Network (RBFNN) technique has evaluated as an alternative method for the prediction of tractor repair and maintenance costs. The study was conducted using empirical data on 60 two-wheel drive tractors from Astan Ghodse Razavi agro-industry in Iran. In this paper, the performance of Basic Back-propagation (BB) training algorithm was a...

Worms spread by scanning for vulnerable hosts across the Internet. In this paper we report a comparative study of three classification schemes for automated portscan detection. These schemes include a simple Fuzzy Inference System (FIS) that uses classical inductive learning, a Neural Network that uses back propagation algorithm and an Adaptive Neuro Fuzzy Inference System (ANFIS) that also emp...

Function approximation is to find the underlying relationship from à given finite input-output data. It has numerous applications such as prediction, pattern recognition, data mining and classification etc. Multilayered feed-forward neural networks (MLFNNs) with the use of back propagation algorithm have been extensively used for the purpose of function approximation recently. Another class of ...

-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 an attempt is made to develop speaker & gender identification system using continuous speech signal spoken in different languages as input. MFCCs and delta-MFCCs are used to build modal for classification . Radial basis function network is used for classification. Here resilient back propagation algorithm used to train Multilingual Speech signal . Two separate modules are used for...

Basic definitions concerning the multi-layer feed-forward neural networks are given. The back-propagation training algorithm is explained. Partial derivatives of the objective function with respect to the weight and threshold coefficients are derived. These derivatives are valuable for an adaptation process of the considered neural network. Training and generalisation of multi-layer feed-forwar...

This paper presents a method of recognition of isolated offline handwritten Devanagari numerals using wavelets and neural network classifier. This method of optical character recognition takes the handwritten numeral image as input. After pre-processing, it is subjected to single level wavelet decomposition using Daubechies-4 wavelet filter. This wavelet decomposition allows viewing the input n...