In this paper a novel neural network (NN) backstepping controller is modified for application to an industrial motor drive system. A control system structure and NN tuning algorithms are presented that are shown to guarantee stability and performance of the closed-loop system. The NN backstepping controller is implemented on an actual motor drive system using a two-PC control system developed a...

The problem of secure data processing by means of a neural network (NN) is addressed. Secure processing refers to the possibility that the NN owner does not get any knowledge about the processed data since they are provided to him in encrypted format. At the same time, the NN itself is protected, given that its owner may not be willing to disclose the knowledge embedded within it. The considere...

Research in AIA neural network is an artificial representation of the human brain that tries to simulate its learning process. An artificial neural network (ANN) is often called a "Neural Network” or simply Neural Net (NN). In this paper I provide the survey which I found more interesting facts in my research. That is 1.The brief study of human brain and nervous system 2. What actually an intel...

As a step towards neural realization of various similarity based algorithms k-NN method has been extended to weighted nearest neighbor scheme. Experiments show that for some datasets significant improvements are obtained. As an alternative to the minimization procedures a best–first search weighted nearest neighbor scheme has been implemented. A feature selection method for k-NN, based on a var...

A sum radial-basis-function neural-network (NN) compensator with computed-torque control and novel weight-tuning algorithms is proposed to improve tracking performance and to account for structured/unstructured uncertainties of robot manipulators. The proposed weight-tuning algorithms do not require the initial NN weights to be small. The bounds of NN weights are guaranteed to be convergent in ...

We propose a hybrid prediction system of neural network and memory-based learning. Neural network (NN) and memory-based reasoning (MBR) are frequently applied to data mining with various objectives. They have common advantages over other learning strategies. NN and MBR can be directly applied to classification and regression without additional transformation mechanisms. They also have strength ...

Due to the complex nature of training neural network (NN), this problem has gained popularity in the nonlinear optimization field. In order to avoid falling into local minimum because of inappropriate initial weights, a number of global search techniques are developed. This paper applies a novel global algorithm, which is electromagnetism-like mechanism (EM) algorithm, to train NN and the EM ba...

The effectiveness of Gabor filters for texture segmentation is well known. In this paper, we propose a texture identification scheme, based on a neural network (NN) using Gabor features. The features are derived from both the Gabor cosine and sine filters. Through experiments, we demonstrate the effectiveness of a NN based classifier using Gabor features for identifying textures in a controlled...

Using a GA as a NN designing tool deals with many aspects. We must decide, among others, about: coding schema, evaluation function, genetic operators, genetic parameters, etc. This paper focuses on an efficiency of NN architecture evolution. We use two main approaches for neural network representation in the form of chromosomes: direct and indirect encoding. Presented research is a part of our ...

In this paper, robust adaptive neural network control is investigated for a class of multi-input-multi-output (MIMO) pure-feedback nonlinear system with unknown nonlinearities. The unknown nonlinearities could be come from unmodeled dynamics, modeling errors, or nonlinear time-varying uncertainties. Based on the backstepping design technique and the universal approximation property of the neura...