This paper deals with the problem of combination of Neural Networks (NN) and traditional statistical pattern classiiers. It is shown that a Neural Network can be used to replace the vector quantizer (VQ) and some feature extraction and feature reduction modules in a discrete pattern recognition system. A criterion for training the NN-weights and the classiier jointly is derived, leading to the ...

Neural networks (NN) has been a popular vibration control method because of its robustness and practicability to reject broad band disturbances for complex systems such as smart structures. However, the benign characteristic of NN, suppressing a wide range frequency of disturbances, may also limit its control performance at specific frequencies and inevitably cause non-minimum output responses ...

Fouling and slagging are some difficulties for the development of biomass as energy potential and to achieve the targets of renewable energy sources utilization. The proper technique to analyze the influence of fouling in a biomass boiler is to monitorize the evolution of heat absorption in heat transfer equipment. Traditional equation-based monitoring techniques have problems to tackle with th...

-The load forecasting is required in power system management and ensures electricity providing for customers. Photovoltaic power forecasting aims to reduce the fuel consumption and play important role in the supervisory control for a hybrid energy system. This paper presents the application of new model using neural networks (NN) and Particle Swarm Optimization (PSO) to determine the net load f...

The study has two aims. The first aim is to propose a family of nonlinear GARCH models that incorporate fractional integration and asymmetric power properties to MS-GARCH processes. The second purpose of the study is to augment the MS-GARCH type models with artificial neural networks to benefit from the universal approximation properties to achieve improved forecasting accuracy. Therefore, the ...

Abstract The bioelectric potentials associated with muscle activity constitute the electromyogram (EMG). EMG signal is being used in biomedical applications in order to detect abnormal muscle electrical activities that occur in many diseases and conditions like muscular dystrophy, inflammation of muscles, pinched nerves, peripheral nerve damages, amyotrophic lateral sclerosis, disc herniation, ...

We describe how multi-stage non-Markovian decision problems can be solved using actor-critic reinforcement learning by assuming that a discrete version of CohenGrossberg node dynamics describes the node-activation computations of a neural network (NN). Our NN (i.e., agent) is capable of rendering the process Markovian implicitly and automatically in a totally model-free fashion without learning...

This survey deals with up-to-date results in the field of hybrid algorithms development of GMDH-type Neural Networks (GMDH-NN) and other methods of Artificial Intelligence (AI) which are successfully used for solving complex economic problems. Such hybrid algorithms are now only in its early stage of active research. General characteristics and main weaknesses of GMDH-NN are firstly presented. ...

Neural Networks (NN) have acquired great success, as they are one of the most rapidly expanding areas. Marketing and specifically Market Segmentation (MS) is one topic that NN can be a useful tool. This work presents an application of NN in MS. A Case Study (CS) in the market of mobile phones is used. In this scenario, a Mobile Company wants to predict what type of mobile phone people desire, i...

the proposed iafc neural networks have both stability and plasticity because theyuse a control structure similar to that of the art-1(adaptive resonance theory) neural network.the unsupervised iafc neural network is the unsupervised neural network which uses the fuzzyleaky learning rule. this fuzzy leaky learning rule controls the updating amounts by fuzzymembership values. the supervised iafc ...