Two new methods to identify the mechanical parameters in induction motor based field oriented drives are presented in this paper. The identified parameters are: the moment of inertia and the viscous damping coefficient. The proposed methods are based on the adaptive linear neuron (ADALINE) networks. The two parameters are derived and optimised during the online training process. During the iden...

In this paper we introduce the generalized feedforward filter, a new class of adaptive filters that combines attractive properties of finite impulse response (FIR) filters with some of the power of infinite impulse response (IIR) filters. A particular case, the gamma filter, generalizes Widrow’s adaptive transversal filter (adaline) to an infinite impulse response filter. Yet, the stability con...

Presents a novel neural-network-based predictor for received power level prediction in direct sequence code division multiple access (DS/CDMA) systems. The predictor consists of an adaptive linear element (Adaline) followed by a multilayer perceptron (MLP). An important but difficult problem in designing such a cascade predictor is to determine the complexity of the networks. We solve this prob...

<p>This paper examines the integration of smart home and solar panel system that is controlled monitored using IoT (internet ofthings). To enable to monitor activity within house act according current conditions, it equipped with several sensors, actuators appliances. All these devices have be connected a communication network, so they can communicate provide services forthe home’s in hab...

Abstract: This paper presents a dual neural network based technique for detecting and classifying the power quality disturbances. In the proposed method, Adaptive Linear Neural Network is used to extract the rms voltage for harmonics and Interharmonics estimations. With the help of these indices, PQ disturbances such as Sag, Swell, Outages are detected and classified, Harmonics and Interharmoni...

The paper aims at extending the scope of application of Widrow-Hoff ’s ADALINE model from binary to gray-level (fuzzy) pattern recognition. The condition of stability for the extended ADALINE model has been derived and the algorithm for training the multilayered feedforward neural net consisting of ADALINE neurons have been presented. The time required for training the neural net is insignifica...

In this paper, a comparison study is presented on artificial intelligence and time series models in 1-hour-ahead wind speed forecasting. Three types of typical neural networks, namely adaptive linear element, multilayer perceptrons, and radial basis function, and ARMA time series model are investigated. The wind speed data used are the hourly mean wind speed data collected at Binalood site in I...