scholarly journals Mixed Modified Recurring Rogers-Szego Polynomials Neural Network Control with Mended Grey Wolf Optimization Applied in SIM Expelling System

Mathematics ◽  
2020 ◽  
Vol 8 (5) ◽  
pp. 754 ◽  
Author(s):  
Der-Fa Chen ◽  
Yi-Cheng Shih ◽  
Shih-Cheng Li ◽  
Chin-Tung Chen ◽  
Jung-Chu Ting
Keyword(s):  
Neural Network ◽  
Control System ◽  
Learning Rule ◽  
Control Performance ◽  
Grey Wolf ◽  
Grey Wolf Optimization ◽  
Good Ability ◽  
Szegő Polynomials ◽  
Szego Polynomials ◽  
Szegö Polynomials

Due to a good ability of learning for nonlinear uncertainties, a mixed modified recurring Rogers-Szego polynomials neural network (MMRRSPNN) control with mended grey wolf optimization (MGWO) by using two linear adjusted factors is proposed to the six-phase induction motor (SIM) expelling continuously variable transmission (CVT) organized system for acquiring better control performance. The control system can execute MRRSPNN control with a fitted learning rule, and repay control with an evaluated rule. In the light of the Lyapunov stability theorem, the fitted learning rule in the MRRSPNN control can be derived, and the evaluated rule of the repay control can be originated. Besides, the MGWO by using two linear adjusted factors yields two changeable learning rates for two parameters to find two ideal values and to speed-up convergence of weights. Experimental results in comparisons with some control systems are demonstrated to confirm that the proposed control system can achieve better control performance.

scholarly journals Permanent-Magnet SLM Drive System Using AMRRSPNNB Control System with DGWO

Energies ◽  
2020 ◽  
Vol 13 (11) ◽  
pp. 2914
Author(s):  
Der-Fa Chen ◽  
Yi-Cheng Shih ◽  
Shih-Cheng Li ◽  
Chin-Tung Chen ◽  
Jung-Chu Ting
Keyword(s):  
Neural Network ◽  
Control System ◽  
Permanent Magnet ◽  
Time Varying ◽  
Nonlinear Friction ◽  
Szegő Polynomials ◽  
Adaptive Laws ◽  
Szego Polynomials ◽  
Szegö Polynomials ◽  
Dynamic Uncertainties

Because permanent-magnet synchronous linear motors (SLM) still exhibit nonlinear friction, ending effects and time-varying dynamic uncertainties, better control performances cannot be achieved by using common linear controllers. We propose a backstepping approach with three adaptive laws and a beating function to control the motion of permanent-magnet SLM drive systems that enhance the robustness of the system. In order to reduce greater vibration in situations with uncertainty actions in the aforementioned control systems, we propose an adaptive modified recurrent Rogers–Szego polynomials neural network backstepping (AMRRSPNNB) control system with three adaptive laws and reimbursed controller with decorated gray wolf optimization (DGWO), in order to handle external bunched force uncertainty, including nonlinear friction, ending effects and time-varying dynamic uncertainties, as well as to reimburse the minimal rebuild error of the reckoned law. In accordance with the Lyapunov stability, online parameter training method of the modified recurrent Rogers–Szego polynomials neural network (MRRSPNN) can be derived by utilizing an adaptive law. Furthermore, to help reduce error and better obtain learning fulfillment, the DGWO algorithm was used to change the two learning rates in the weights of the MRRSPNN. Finally, the usefulness of the proposed control system is validated by tested results.

scholarly journals Grey Wolf Optimization algorithm with Discrete Hopfield Neural Network for 3 Satisfiability analysis

2021 ◽  
Vol 1821 (1) ◽  
pp. 012038
Author(s):  
Mohd. Asyraf Mansor ◽  
Mohd Shareduwan Mohd Kasihmuddin ◽  
Saratha Sathasivam
Keyword(s):  
Neural Network ◽  
Optimization Algorithm ◽  
Hopfield Neural Network ◽  
Grey Wolf ◽  
Grey Wolf Optimization

A novel hybrid grey wolf optimization algorithm and artificial neural network technique for stochastic unit commitment problem with uncertainty of wind power

2021 ◽  
pp. 014233122110019
Author(s):  
C. Venkatesh Kumar ◽  
M. Ramesh Babu
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Wind Power ◽  
Optimization Algorithm ◽  
Unit Commitment ◽  
Operating Cost ◽  
Grey Wolf ◽  
Grey Wolf Optimization ◽  
Stochastic Unit Commitment ◽  
Hybrid Methodology

The unit commitment (UC) is highly complex to solve the increasing integrations of wind farm due to intermittent wind power fluctuation in nature. This paper presents a hybrid methodology to solve the stochastic unit commitment (SUC) problem depending on binary mixed integer generator combination with renewable energy sources (RESs). In this combination, ON/OFF tasks of the generators are likewise included to satisfy the load requirement as for the system constraints. The proposed hybrid methodology is the consolidation of grey wolf optimization algorithm (GWOA) and artificial neural network (ANN), hence it is called the hybrid GWOANN (HGWOANN) technique. Here, the GWOA algorithm is used to optimizing the best combination of thermal generators depending on uncertain wind power, minimum operating cost and system constraints – that is, thermal generators limits, start-up cost, ramp-up time, ramp-down time, etc. ANN is utilized to capture the uncertain wind power events, therefore the system ensures maximal application of wind power. The combination of HGWOANN technique guarantees the prominent use of sustainable power sources to diminish the thermal generators unit operating cost. The proposed technique is implemented in MATLAB/Simulink site and the efficiency is assessed with different existing methods. The comparative analysis demonstrates that the proposed HGWOANN approach is proficient to solve unit commitment problems and wind integration. Here, the HGWOANN method is compared with existing techniques such as PSO, BPSO, IGSA to assess the overall performance using various metrics viz. RMSE, MAPE, MBE under 50 and 100 count of trials. In the proposed approach, the range of RMSE achieves 9.26%, MAPE achieves 0.95%, MBE achieves 1% in 50 count of trials. Moreover, in 100 count of trials, the range of RMSE achieves 7.38%, MAPE achieves 1.91%, MBE achieves 2.87%.

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