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Particle swarm optimization for neural network learning enhancement
Published 2006“…Two programs have been developed; Particle Swarm Optimization Feedforward Neural Network (PSONN) and Genetic Algorithm Backpropagation Neural Network (GANN). …”
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Optimal power flow using hybrid firefly and particle swarm optimization algorithm
Published 2020“…In this paper, a novel, effective meta-heuristic, population-based Hybrid Firefly Particle Swarm Optimization (HFPSO) algorithm is applied to solve different non-linear and convex optimal power flow (OPF) problems. …”
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A comparative study of multi-objective optimal power flow based on particle swarm, evolutionary programming, and genetic algorithm
Published 2015“…This paper compares the performance of three population-based algorithms including particle swarm optimization (PSO), evolutionary programming (EP), and genetic algorithm (GA) to solve the multi-objective optimal power flow (OPF) problem. …”
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Image clustering comparison of two color segmentation techniques
Published 2010“…Finally, the algorithm found, which would solve the image segmentation problem.…”
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Dynamic control and performance of dual active bridge converter based particle swarm optimization
Published 2022“…This paper presents analysis of the dynamic response of the isolated bidirectional dual active bridge (DAB) DC-DC converter using particle swarm optimization (PSO) algorithm. …”
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Conference or Workshop Item -
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Particle swarm optimisation for reactive power compensation on Oman 6 bus electrical grid
Published 2021“…Reduction of system active power loss is the goal of the function in the projected algorithm. …”
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Forecasting of photovoltaic output using hybrid particle swarm optimization-artificial neural network model / Muhamad Faizol Adli Abdullah
Published 2010“…In Backpropagation Neural Network (BPNN), there are many elements to be considered such as the number of input, hidden and output nodes, learning rate, momentum rate, bias, minimum error and activation/transfer functions. These entire elements will affect the speed of natural network learning. …”
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Automatic Number Plate Recognition on android platform: With some Java code excerpts
Published 2016“…On the other hand, the traditional algorithm using template matching only obtained 83.65% recognition rate with 0.97 second processing time. …”
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Book -
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Advanced Pareto front non-dominated sorting multi-objective particle swarm optimization for optimal placement and sizing of distributed generation
Published 2016“…The distributed generation consists of single and multiple numbers of active power DG, reactive power DG and simultaneous placement of active-reactive power DG. …”
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Advanced Pareto front non-dominated sorting multi-objective particle swarm optimization for optimal placement and sizing of distributed generation
Published 2016“…The distributed generation consists of single and multiple numbers of active power DG, reactive power DG and simultaneous placement of active-reactive power DG. …”
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Development of seven segment display recognition using TensorFlow on Raspberry Pi
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Proceeding Paper -
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Multi leader particle swarm optimization for optimal placement and sizing of multiple distributed generation for a micro grid
Published 2023“…Initially, the Particle Swarm Optimization was implemented to establish the optimal sizes of DGs and the performance of the implemented algorithm was analyzed and quantified. …”
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An improved grey wolf with whale algorithm for optimization functions
Published 2022“…The performance of the proposed algorithm is tested and evaluated on five benchmarked unimodal and five multimodal functions. …”
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An improved artificial bee colony algorithm for training multilayer perceptron in time series prediction
Published 2014“…Most of the training algorithms focus on weight values, activation functions, and network structures for providing optimal outputs. …”
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Y-type Random 2-satisfiability In Discrete Hopfield Neural Network
Published 2024“…During the retrieval phase, a new activation function and Swarm Mutation were proposed to ensure the diversity of the neuron states. …”
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Differential evolution for neural networks learning enhancement
Published 2008“…These algorithms can be used successfully in many applications requiring the optimization of a certain multi-dimensional function. …”
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