Search Results - (( (variable OR variables) control optimization algorithm ) OR ( java machine learning algorithm ))
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Study of nature inspired computing (NIC) technique for optimal reactive power dispatch problems
Published 2017“…The proposed algorithms are tested on five different case studies which are IEEE 30-bus system with 13 control variables, IEEE 30-bus system with 19 control variables, IEEE 30-bus system with 25 control variables, IEEE 57-bus system with 25 control variables and IEEE 118-bus system with 77 control variables. …”
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2
Optimization of PID parameters for hydraulic positioning system utilizing variable weight Grey-Taguchi and particle swarm optimization
Published 2014“…Particle swarm optimization algorithm (PSO) is one of the artificial intelligence methods. …”
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3
Optimization of PID Parameters Utilizing Variable Weight Grey-Taguchi Method and Particle Swarm Optimization
Published 2017“…Particle swarm optimization algorithm (PSO) is one of the artificial intelligence methods. …”
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A meta-heuristics based input variable selection technique for hybrid electrical energy demand prediction models
Published 2017“…These models are developed by integrating multilayer perceptron neural network and evolutionary optimization techniques. Genetic algorithm and simulated annealing techniques are used to optimize the control parameters of the neural network. …”
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5
Efficiency optimization of variable speed induction motor drive using online backpropagation
Published 2006“…In this paper the designs of a back propagation based efficiency optimization control (BPEOC) for variable speed compressor induction motor drive is described. …”
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Cuckoo search algorithm as an optimizer for optimal reactive power dispatch problems
Published 2017“…This paper presents the application of Cuckoo Search Algorithm (CSA) in optimizing the control variables of power system operation in solving the optimal reactive power dispatch (ORPD) problem. …”
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7
Optimization Of Sliding Mode Control Using Particle Swarm Algorithm For An Electro-Hydraulic Actuator System
Published 2016“…The dynamic parts of electro-hydraulic actuator(EHA) system are widely applied in the industrial field for the process that exposed to the motion control.In order to achieve accurate motion produced by these dynamic parts,an appropriate controller will be needed.However,the EHA system is well known to be nonlinear in nature.A great challenge is carried out in the EHA system modelling and the controller development due to its nonlinear characteristic and system complexity.An appropriate controller with proper controller parameters will be needed in order to maintain or enhance the performance of the utilized controller.This paper presents the optimization on the variables of sliding mode control (SMC) by using Particle Swarm Optimization (PSO) algorithm.The control scheme is established from the derived dynamic equation which stability is proven through Lyapunov theorem.From the obtained simulation results,it can be clearly inferred that the SMC controller variables tuning through PSO algorithm performed better compared with the conventional proportionalintegral-derivative (PID) controller.…”
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Automatic control of flotation process using computer vision
Published 2015“…A control strategy based on froth model was then designed in order to optimize the visual characteristics of froth, which lead to the control of the metallurgical parameters in an indirect manner. …”
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Single and Multiple variables control using Tree Physiology Optimization
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Teaching and learning via chatbots with immersive and machine learning capabilities
Published 2019“…These chatbots support learning of Java via problem-solving steps through “learning by doing”. …”
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Application of optimized higher order sliding mode control on MEMS optical switch
Published 2012“…A Robust Exact Differentiator is used to estimate time derivatives of the sliding variable. A novel application of Particle Swarm Optimization (PSO) is introduced for optimal tuning of the HOSM controller. …”
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12
Global Algorithms for Nonlinear Discrete Optimization and Discrete-Valued Optimal Control Problems
Published 2009“…Some optimal control problems involve a control which takes values from a discrete set. …”
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13
Maximizing power generation in variable speed micro-hydro with power point tracking
Published 2022“…Conventional variable speed micro-hydro control systems suffer from non-optimal input control. …”
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Proceedings -
14
Harmony search-based robust optimal controller with prior defined structure
Published 2013“…In this approach, a combination of interacting two levels HS optimization algorithm is presented. In the first level, a new method for analytical formulation of integral square error cost function based on controller variables is elaborated for performance evaluation purposes by the proposed optimization algorithm. …”
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15
To develop an efficient variable speed compressor motor system
Published 2007“…To achieve a robust controller from variation of motor parameters, a real-time or on-line learning algorithm based on a second order optimization Levenberg-Marquardt is employed. …”
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A Hybrid Adaptive Leadership GWO Optimization with Category Gradient Boosting on Decision Trees Algorithm for Credit Risk Control Classification
Published 2024“…However, compared with the traditional risk control algorithm (logistic regression algorithm), CatBoost algorithm also needs to have the advantages of high efficiency, low algorithm complexity and strong interpretable ability. …”
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On the optimal control of the steel annealing processes as a two-stage hybrid systems via PSO algorithms
Published 2009“…The computation of optimal control variables for a two-stage steel annealing process which comprises of one or more furnaces is proposed in this paper. …”
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Application of genetic algorithm for optimal voltage control of the power system / Mohd Supian Yahya
Published 1998“…This project uses genetic algorithm (GA) for optimal voltage control of the power system and automatically will minimum a system loss. …”
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20
Neural network based model predictive control for a steel pickling process
Published 2009“…The Levenberg-Marquardt algorithm is used to train the process models. In the control (MPC) algorithm, the feedforward neural network models are used to predict the state variables over a prediction horizon within the model predictive control algorithm for searching the optimal control actions via sequential quadratic programming. …”
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