A back-propagation neural network is applied to a nonlinear self-tuning tracking problem. Traditional self-tuning adaptive control techniques can only deal with linear systems or some special nonlinear systems. The emerging back-propagation neural networks have the capability to learn arbitrary nonlinearity and show great potential for adaptive control applications. A scheme for combining back-...

This paper used error back propagation algorithm and regression analysis to analyze and predict untranslated and translated Nigeria Stock Market Price (NSMP). Nigeria stock market prices were collected for the periods of seven hundred and twenty days and grouped into untranslated and translated train, validation and test data. A zero mean unit variance transformation was used to normalize the i...

The supervised learning of the discriminative convolutional neural network (ConvNet or CNN) is powered by back-propagation on the parameters. In this paper, we show that the unsupervised learning of a popular top-down generative ConvNet model with latent continuous factors can be accomplished by a learning algorithm that consists of alternatively performing back-propagation on both the latent f...

The main contribution of the present paper is to train efficient neural networks for seismic design of double layer grids subject to multiple-earthquake loading. As the seismic analysis and design of such large scale structures require high computational efforts, employing neural network techniques substantially decreases the computational burden. Square-on-square double layer grids with the va...

high processing loads, need for complicated and frequent updating, and high false alarm are some of the challenges in designing anomaly detection and misuse detection systems. we propose a new network-based intrusion detection system (ids) that resolves such shortcomings. our scheme fuses anomaly detection and misuse detection systems, which has not been utilized so far in existing systems. in ...

Multilayered feed-forward neural networks trained with back-propagation algorithm are one of the most popular “online” artificial neural networks. These networks are showing strong inherit parallelism because of the influence of high number of simple computational elements. So it is natural to try to implement this kind of parallelism on parallel computer architecture. The Parallel Hybrid Ring ...

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...