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Nature presents an infinite source of inspiration for computational models and paradigms, in particular for researchers associated with the area known as natural computing. The simultaneous optimization of the architectures and weights of artificial neural networks (ANNs) through biologically inspired algorithms is an interesting approach for obtaining efficient networks with relatively good generalization...
This paper introduce a novel design of the static VAR compensator (SVC) controller for damping power system oscillations. A multi layer neural network model tuned by Grey Wolf Optimization algorithm (GWO) is investigated and presented. GWO search algorithm is used to optimized all the connection of weights and biases for the artificial neural network. The proposed approach depends up on the expected...
This study investigates the evaluation of machine learning models based on multiple criteria. The criteria included are: predictive model accuracy, model complexity, and algorithmic complexity (related to the learning/adaptation algorithm and prediction delivery) captured by monitoring the execution time. Furthermore, it compares the models generated from optimising the criteria using two approaches...
Backpropagation algorithm is widely used to solve many real-world problems, using the concept of Multilayer Perceptron. However, main disadvantages of Backpropagation are the convergence rate of it being relatively slow, and it is often trapped in the local minima. To solve this problem, it is found in the literatures, an evolutionary algorithm such as Particle Swarm Optimization algorithm is applied...
Permanent Magnet Synchronous Motors (PMSMs) are prone to instability when operated open loop in variable speed drives. This paper presents a real-time control strategy based on Adaptive Neural Networks (ANN) for efficiency optimisation of a previously reported drive system incorporating a high-field PMSM. Stability constraints of the drive system are used by the proposed controller to maintain both...
In algorithms design, one of the important aspects is to consider efficiency. Many algorithm design paradigms are existed and used in order to enhance algorithms' efficiency. Opposition-based Learning (OBL) paradigm was recently introduced as a new way of thinking during the design of algorithms. The concepts of opposition have already been used and applied in several applications. These applications...
Artificial Neural Networks (ANN) have lately received much attention, and a great number of papers have reported successful experiments and practical tests. This paper presents the development of a simple ANN topology for load forecasting model with much improved accuracy for the Regional Power Control Centre of Saudi Electricity Company. The proposed system is based on optimising the initial random...
This paper presents artificial neural networks and particle swarm optimization (ANN-PSO) based short-term load forecasting model with improved generalization technique for the regional power control center of Saudi Electricity Company, Western Operation Area (SEC-WOA) of Saudi Arabia. Weather, load demand, wind speed, wind direction, heat, sunlight, etc. are quite different in a desert land than other...
Several watermarking schemes have been proposed with the purpose of copyright protection and access control for multimedia objects. In this paper, an optimal discrete wavelet transform-singular value decomposition (DWT-SVD) based image watermarking scheme using particle swarm optimizer (PSO) is presented. Proposed DWT-SVD based watermarking algorithm initially decomposes the host image into subbands,...
This paper presents a formulation of security-constrained unit commitment (SCUC) problem based on mixed integer programming (MIP) method with considering prohibited operating zone limits of thermal and hydro units. The objective of SCUC problem is to obtain minimum system operating cost while maintaining the system security. Thermal and hydro units in power system studies are assumed to possess smooth...
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