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 the use of back propagation neural network to implement voice recognition. The focus is to identify voice patterns of different people so as to recognize their voices electronically. The signals corresponding to a text phrase of a group of people are recorded in voice files on a computer using sound recording software. The information in these files is converted from time do...

Mutual information neuro-evolutionary system (MINES) presents a novel self-governing approach to determine the optimal quantity and connectivity of the hidden layer of a three layer feed-forward neural network founded on theoretical and practical basis. The system is a combination of a feed-forward neural network, back-propagation algorithm, genetic algorithm, mutual information and clustering....

Diabetes Mellitus is a chronic, lifelong metabolism disorder that affects the ability of the body system to use the energy found in food. People living with high blood sugar will experience polyuria (frequent urination), which will make them to become increasingly thirsty (polydipsia) and hungry (polyphagia) .The improper management of this disease can lead to complication such as cardiovascula...

Recent research has focused on feed-forward networks with complex weights and activation values such as GK92, Hir92b, Hir92a, Hir93]. This paper extends this formalism to feed-forward networks with weight and activation values taken from a Cliiord algebra (see also PB92, PB94b]). A Cliiord algebra is a multi-dimensional generalization of the complex numbers and the Quaternions. Essentially a Cl...

Back-propagation with gradient method is the most popular learning algorithm for feed-forward neural networks. However, it is critical to determine a proper fixed learning rate for the algorithm. In this paper, an optimized recursive algorithm is presented for online learning based on matrix operation and optimization methods analytically, which can avoid the trouble to select a proper learning...

This paper presents a neurocomputational model for estimation of feed-position in circular microstrip antenna. The difficulty in computing the feed position in circular microstrip antenna lies due to the involvement of a large number of physical parameters including their associated optimal values. It is indeed very difficult to formulate an exact numerical solution merely on practical observat...

This paper explores the effect of initial weight selection on feed-forward networks learning simple functions with the back-propagation technique. We first demonstrate, through the use of Monte Carlo techniques, that the magnitude of the initial condition vector (in weight space) is a very significant parameter in convergence time variability. In order to further understand this result, additio...

In the present paper, we are use the neural network to recognize the character. In this paper it is developed 0ff-line strategies for the isolated handwritten English character (A TO Z) and (0 to 9) .This method improves the character recognition method. Preprocessing of the Character is used binarization, thresolding and segmentation method .The proposed method is based on the use of feed forw...