Nonlinear systems modelling based on self-organizing fuzzy neural network with hierarchical pruning scheme

Zhou, Hongbiao; Zhang, Yu; Duan, Weiping; Zhao, Huanyu

doi:10.1016/j.asoc.2020.106516

Cited by 52 publications

(18 citation statements)

References 67 publications

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“…In recent years, with the improvement of power system operation requirements, the reactive power optimization has evolved from a single-objective optimization problem to a multiobjective optimization problem [7]. Multi-objective reactive power optimization is a multi-objective, multiconstraint, multi-variable hybrid nonlinear optimization problem [8]- [10]. At present, algorithms for addressing multi-objective problems are mainly divided into two categories: conventional algorithms and intelligent algorithms.…”

Section: Introductionmentioning

confidence: 99%

Multi-Objective Reactive Power Optimization Based on Improved Particle Swarm Optimization With ε-Greedy Strategy and Pareto Archive Algorithm

Liu

Zhang

Fang³

et al. 2021

IEEE Access

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This paper proposes combining an improved particle swarm optimization and Pareto archive algorithm to solve the multi-objective reactive power optimization problem. The idea of ε-greedy strategy is adopted and designed to improve particle swarm optimization algorithm. It makes some particles have stronger global search capability, meanwhile, others have stronger local search capability during the whole iteration process. Henceforth, the strategy significantly explores the possibility of optimal solution in local space at the early stage of the iteration, in addition, it mitigates the tendency to fall into the local optimal solution at the later stage of the iteration. The Pareto optimal solution selection problem is solved by minimizing the sum of the difference between each objective function and its optimal solution. The proposed approach is tested on IEEE39-bus and IEEE118-bus system, and it is demonstrated that the proposed approach not only restores the nodes voltage to the normal range and achieves better value for each objective function, but also outperforms other algorithms including standard particle swarm optimization and non-dominated sorting genetic algorithm Ⅱ(NSGA-Ⅱ).INDEX TERMS multi-objective reactive power optimization, particle swarm optimization, ε-greedy strategy, Pareto archive algorithm, voltage control.

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Section: Introductionmentioning

confidence: 99%

Multi-Objective Reactive Power Optimization Based on Improved Particle Swarm Optimization With ε-Greedy Strategy and Pareto Archive Algorithm

Liu

Zhang

Fang³

et al. 2021

IEEE Access

View full text Add to dashboard Cite

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“…Because the thresholds utilized in the generating and pruning algorithms should were predefined as constants by the trialand-error process, and it is not easy to determine the corresponding reasonable values even for experienced designers. To solve this problem, Zhou et al [27] designed a self-organizing FNN with a hierarchical pruning scheme to avoid the performance degeneration caused by the improper pruning threshold in the traditional error-reduction-ratio (ERR) method, and the effectiveness of the proposed scheme was verified in a wastewater treatment process. However, the accumulative density value over several sampling periods was used to measure the importance of the existing rules, which is not suitable for a real control system with a small time constant.…”

Section: Introductionmentioning

confidence: 99%

Self-Constructing Fuzzy-Neural-Network-Imitating Sliding-Mode Control for Parallel-Inverter System in Grid-Connected Microgrid

Yang

Wai²

2021

IEEE Access

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This study mainly develops a self-constructing fuzzy neural network (SFNN) with the structure and parameter self-learning abilities to imitate a sliding-mode control (SMC), and implements the grid-connected current tracking control for a parallel-inverter system in a grid-connected microgrid (MG) with a master-slave current sharing strategy. In the proposed SFNN-imitating SMC (SFNNISMC) scheme, the initial nodes of the input layer are determined by the number of the grid-connected inverter units, and the rules of the membership layer are self-generated online from null online according to the instantaneous inputs based on the dynamic rule-generating scheme. Moreover, a dynamic Petri net is introduced to implement the pruning mechanism, and is utilized to recall the rules corresponding to the reconnected slave inverters. Only the parameters of favorable rules fired by the Petri net are updated online instead of all the parameters, which can significantly alleviate the computational burden of parameter learning. In addition, the projection algorithm and the Lyapunov stability theorem are adopted to ensure the convergence of the parameter adaptation and the grid-connected current-tracking errors. Furthermore, the rule evolutions of the proposed SFNNISMC in the structure self-learning process are illustrated in numerical simulations. The superiority of the proposed SFNNISMC framework is further validated by experimental comparisons with a proportional-integral control (PIC) strategy, an SMC scheme and an adaptive FNN-imitating SMC (AFNNISMC) framework with a fixed network structure from the previous research to be carried out on a parallel-inverter system with two single inverters.

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“…Zhang et al [23] proposed a multi-variable direct self-organizing fuzzy network for modeling wastewater treatment process. Zhou et al [24] presented self-organizing fuzzy network with hierarchical pruning scheme for modeling nonlinear systems in industrial processes. Therefore, the proposed DRFNN and SOFNN were compared in the experiment part.…”

Section: Introductionmentioning

confidence: 99%

An Efficient Dynamic Regulated Fuzzy Neural Network for Human Motion Retrieval and Analysis

2021

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Human motion retrieval and analysis is a useful means of activity recognition to 3D human bodies. An efficient method is proposed to estimate human motion by using symmetric joint points and limb features of various limb parts based on regression task. We primarily obtain the 3D coordinates of symmetric joint points based on the located waist and hip points. By introducing three critical feature points on torso and symmetric joint points’ matching on motion video sequences, the 3D coordinates of symmetric joint points and its asymmetric limb features will not be affected by shading and interference of limb on different postures. With the asymmetric limb features of various human parts, a dynamic regulated Fuzzy neural network (DRFNN) is proposed to estimate human motion for different asymmetric postures using learning algorithm of network parameters and weights. Finally, human sequential actions corresponding to different asymmetric postures are presented according to the best retrieval results by DRFNN based on 3D human action database. Experiments show that compared with the traditional adaptive self-organizing fuzzy neural network (SOFNN) model, the proposed algorithm has higher estimation accuracy and better presentation results compared with the existing human motion analysis algorithms.

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Nonlinear systems modelling based on self-organizing fuzzy neural network with hierarchical pruning scheme

Cited by 52 publications

References 67 publications

Multi-Objective Reactive Power Optimization Based on Improved Particle Swarm Optimization With ε-Greedy Strategy and Pareto Archive Algorithm

Multi-Objective Reactive Power Optimization Based on Improved Particle Swarm Optimization With ε-Greedy Strategy and Pareto Archive Algorithm

Self-Constructing Fuzzy-Neural-Network-Imitating Sliding-Mode Control for Parallel-Inverter System in Grid-Connected Microgrid

An Efficient Dynamic Regulated Fuzzy Neural Network for Human Motion Retrieval and Analysis

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