This work presents a method to increased the face recognition accuracy using a combination of Wavelet, PCA, and Neural Networks. Preprocessing, feature extraction and classification rules are three crucial issues for face recognition. This paper presents a hybrid approach to employ these issues. For preprocessing and feature extraction steps, we apply a combination of wavelet transform and PCA....

A new fault detection method using neural-networks-augmented state observer for nonlinear systems is presented in this paper. The novelty of the approach is that instead of approximating the entire nonlinear system with neural network, we only approximate the unmodeled part that is left over after linearization, in which a radial basis function RBF neural network is adopted. Compared with conve...

This paper introduces a novel method for human face recognition that employs a set of different kind of features from the face images with Radial Basis Function (RBF) neural network called the Hybrid N-Feature Neural Network (HNFNN) human face recognition system. The face image is projected in each appropriately selected transform methods in parallel. The output of the RBF classifiers are fused...

A new method that researching fault diagnosis of high-voltage (HV) circuit breaker (CB) is proposed. The method combines Wavelet Packet (WP) with Radical Basis Function (RBF) Neural Network (NN). Firstly, by applying the theory of WP decomposition and reconstruction, the mechanical vibration signal of CB was decomposed into different frequency bands, and the coefficients are reconstructed in th...

The traditional error of the back-propagation algorithm multilayer feed-forward network (BP neural network), there are the flaws of a slow convergence of forecast, getting local minimum solutions easily, and forecast accuracy rate is not high. This paper proposes a new approach which is the combination of hierarchical genetic algorithm and least squares method to optimize the RBF neural network...

Neural network process modelling needs the use of experimental design and studies. A new neural network constructive algorithm is proposed. Moreover, the paper deals with the influence of the parameters of radial basis function neural networks and multilayer perceptrons network in process modelling. Particularly, it is shown that the neural modelling, depending on learning approach, cannot be a...

Noisy distance measurements are a pervasive problem in localization in wireless sensor networks. Neural networks are not commonly used in localization, however, our experiments in this paper indicate neural networks are a viable option for solving localization problems. In this paper we qualitatively compare the performance of three different families of neural networks: Multi-Layer Perceptron ...

Yaobin Qin [email protected] Supervisor: Pro.lilja Department of Electrical and Computer Engineering Abstract Neural networks are family statistical learning algorithms and structures and are used to estimate or approximate functions and pattern classification. The Neural network system is constructed through interconnected neurons and training weights. The paper will present the improvement of ...

In this paper the Rank M-Type Radial Basis Function (RMRBF) neural network is used for the classification of Pap smear microscopic images. Simulation results indicate that the proposed neural network consistently outperforms the RBF network in terms of classification capabilities.

This paper presents a model reference adaptive PD control scheme based on RBF neural network for the greenhouse climate control problem. A model of nonlinear conservation laws of enthalpy and matter between numerous system variables affecting the greenhouse climate is used to validate the proposed control scheme. Compared with the conventional adaptive PD control scheme based on RBF neural netw...