Journal of Intelligent & Fuzzy Systems - Volume 32, issue 6

Authors: Wang, Yizhao | Wang, Lide | Yan, Xiang | Shen, Ping

Article Type: Research Article

Abstract: This paper addresses the scheduling problem of periodic information in multifunction-vehicle-bus networked control system (MNCS). To deal with this issue, a novel fuzzy immune particle swarm optimization (FIPSO) algorithm is proposed to eliminate the premature convergence of standard PSO algorithms in solving complex problem, which uses the fuzzy particle swarm optimization (FPSO) algorithm and the immune particle swarm optimization (IPSO) algorithm for reference. Through designing the fuzzy logic controller (FLC), the inertia weight and the immune-execute factor are regulated dynamically on the basis of the evolutionary time, the variation of average fitness value and the population diversity. The FLC also …guides evolutionary directions and decides whether to execute immune operations, making the algorithm converge for many times. The simulation and calculation results of the scheduling example show that FIPSO algorithm has strong global search ability, good stability and better optimization performance. Particularly, based on the variable individual periods, the co-design of scheduling and control in MNCS is implemented by applying the FIPSO algorithm. Show more

Keywords: Particle swarm optimization, multifunction vehicle bus (MVB), scheduling, networked control system (NCS), co-design

DOI: 10.3233/IFS-152067

Citation: Journal of Intelligent & Fuzzy Systems, vol. 32, no. 6, pp. 3797-3807, 2017

Authors: Chiu, Chih-Hui | Peng, Ya-Fu | Sun, Chung-Hsun

Article Type: Research Article

Abstract: In this work, an intelligent decoupled backstepping control system (IDBCS) is proposed for mobile inverted pendulums (MIPs) real-time control. This control system combined with adaptive output recurrent cerebellar model articulation controller (AORCMAC) and H ∞ control theory. The AORCMAC is designed to imitate an ideal backstepping controller, and the H ∞ controller is used to mitigate the effect of the approximation errors and outer disturbances. The decoupled method provides an easy way to achieve asymptotic stability control for a fourth-order nonlinear mobile inverted pendulum system. The concept of the decoupled approach is to decouple the whole system into …two subsystems such that each subsystem has an individual control target. Then, the secondary subsystem provides information for the main subsystem, which generates a control action to make both subsystems move to their targets, respectively. In other words, it means that a fourth-order MIP system can be controlled well based on a second-order dynamic model. Moreover, all the adaptation laws of the IDBCS are obtained based on Lyapunov stability criterion, Taylor linearization technique and H ∞ control technique, so that the stability of the system can be guaranteed. Experiment results show that the MIP can stand stably when it moves toward a given position. Show more

Keywords: Mobile inverted pendulum, backstepping tracking control, H∞ control, output recurrent cerebellar model articulation control, decoupled intelligent controller

DOI: 10.3233/IFS-162109

Citation: Journal of Intelligent & Fuzzy Systems, vol. 32, no. 6, pp. 3809-3820, 2017

Authors: Tang, Jian | Davvaz, Bijan | Xie, Xiangyun

Article Type: Research Article

Abstract: In this paper, as a generalization of the concept of quasi-Γ-hyperideals of Γ-semihypergroups to ordered Γ-semihypergroup theory, the concept of quasi-Γ-hyperideals of ordered Γ-semihypergroups is introduced, and related properties are discussed. Furthermore, we define and study fuzzy quasi-Γ-hyperideals of ordered Γ-semihypergroups. In particular, we investigate the structure of fuzzy quasi-Γ-hyperideal generated by a fuzzy subset in an ordered Γ-semihypergroup. In addition, we also introduce the concepts of completely prime, weakly completely prime and completely semiprime fuzzy quasi-Γ-hyperideals of ordered Γ-semihypergroups, and characterize bi-regular ordered Γ-semihypergroups in terms of completely semiprime fuzzy quasi-Γ-hyperideals. Finally, several characterizations of regular and intra-regular ordered …Γ-semihypergroups by the properties of fuzzy quasi-Γ-hyperideals are given. Show more

Keywords: Ordered Γ-semihypergroup, ordered fuzzy point, (fuzzy) quasi-Γ-hyperideal, weakly completely prime fuzzy quasi-Γ-hyperideal, completely semiprime fuzzy quasi-Γ-hyperideal

DOI: 10.3233/IFS-162117

Citation: Journal of Intelligent & Fuzzy Systems, vol. 32, no. 6, pp. 3821-3838, 2017

Authors: Bahmani-Firuzi, Bahman | Khorshidi, Reza

Article Type: Research Article

Abstract: The future smart grids will contain a high number of Plug-in Electric Vehicles (PEVs) that will move in the grid widely. The high penetration of these devices will bring new challenges regarding the optimal operation and management of the system. In this way, this paper proposes a realistic framework to first model PEVs movements in the grid and second schedule their movement for minimizing the costs. The cost function consists of the total network cost for supplying the electric loads and PEVs for 24 hour time horizon. According to the high complexities of the problem, a new optimization framework based …on teacher learning algorithm (TLO) with a new modification method is proposed to search the problem space thoroughly. The feasibility and satisfying performance of the proposed optimization framework are examined on the IEEE test system. Show more

Keywords: Plug-in Electric Vehicle (PEV), Vehicle-to-Grid (V2G), Modified Teacher Learning Optimization (MTLO)

DOI: 10.3233/IFS-162145

Citation: Journal of Intelligent & Fuzzy Systems, vol. 32, no. 6, pp. 3839-3846, 2017

Authors: Goswami, Saptarsi | Chakrabarti, Amlan | Chakraborty, Basabi

Article Type: Research Article

Abstract: Feature elimination happens because either the features are irrelevant or they are redundant. The major challenge with feature selection for clustering is that relevance of a feature is not well defined. In this paper, an attempt to address this gap is made. Feature relevance is firstly defined in terms of Variability Score (VSi ), a novel score which measures a feature’s contribution to the overall variability of the dataset. Secondly, feature relevance is evaluated using entropy. VSi is a multivariate measure of feature relevance, where as entropy is univariate. Both of them have been used in a greedy forward …search to select optimal feature subset (FSELCET –VS, FSELECT –EN). Redundancy is handled using Pearson’s correlation coefficient. Dataset characteristics also influence result. Therefore it is recommended to apply both and adopt the best for that particular dataset. Extensive empirical study over thirty publicly available datasets show that the proposed method produces better performance compared to a few state of the art methods. The average feature reduction produced is 44%. No statistically significant reduction in performance (t  = –0.35, p  = 0.73) when compared with all features was observed. Moreover, the proposed method is shown to be relatively computationally inexpensive as well. Show more

Keywords: Feature selection, correlation, entropy, principal components analysis, greedy forward

DOI: 10.3233/IFS-162156

Citation: Journal of Intelligent & Fuzzy Systems, vol. 32, no. 6, pp. 3847-3858, 2017

Authors: Simab, Mohsen | Chatrsimab, Seyavash | Yazdi, Sepide | Simab, Ali

Article Type: Research Article

Abstract: In this paper, an integrated algorithm has been proposed for ranking contingencies in the deregulated network. The network security and economical indices should be considered when dealing with market environment. Locational marginal price and congestion cost indices are the best signals to completely illustrate the market operation. In this paper, voltage violation, line flow violation, locational marginal price and congestion cost indices have been simultaneously considered to rank the contingencies. This algorithm uses neural networks method to estimate the power system parameters (locational marginal price, bus voltage magnitudes and angles). The efficiency of each of contingencies was calculated using data …envelopment analysis and this index was employed for ranking. The efficiency of each contingency shows its severity and indicates that it affects network security and economic indices concurrently. Considering the proposed formulation for data envelopment analysis, the efficiency of a contingency will be higher if the calculated indices for that contingency are higher. More efficiency leads to increased severity of the contingency and shows that the contingency has concurrently more affected network security and economic indices. The proposed algorithm has been tested on IEEE 30-bus test power system. Simulation results show the high efficiency of the algorithm. Show more

Keywords: Contingency ranking, deregulated network, neural network, network security indices, data envelopment analysis

DOI: 10.3233/IFS-162169

Citation: Journal of Intelligent & Fuzzy Systems, vol. 32, no. 6, pp. 3859-3866, 2017

Authors: Yu, Bin | Li, Qingguo

Article Type: Research Article

Abstract: This article is to introduce the concept of a rough soft lattice, which is an extended concept of a rough soft set and a lattice. We study roughness in soft lattices with respect to congruence approximation spaces. Some new rough soft operations are explored. In particular, lower and upper rough soft lattices and some special rough soft lattices are investigated. Finally, we give a decision method based on rough soft lattices.

Keywords: Lattice, rough soft set, rough soft lattice, rough soft distributive(modular) lattice, decision making

DOI: 10.3233/IFS-162173

Citation: Journal of Intelligent & Fuzzy Systems, vol. 32, no. 6, pp. 3867-3878, 2017

Authors: Ke, Xiaolu | Ma, Liyao | Wang, Yong

Article Type: Research Article

Abstract: The belief rule based (BRB) methodology is developed from the traditional IF–THEN rule based system and evidential reasoning (ER) approach. It can be used to model complicated nonlinear causal relationships between antecedent attributes and consequents under different types of uncertainty. In this paper, we present a new BRB structure for modelling uncertain nonlinear systems. It uses the weighted averaging operator to replace the ER approach in the inference process. With this change, the BRB structure could be simplified and faster speeds are obtained in both training and inference process, while universal approximation capability is maintained. By using the consequents of …the new BRB model, an approach for reducing possibly redundant referential values of antecedent attributes is proposed for point estimate. Case studies are conducted on three well known benchmark datasets to compare the new model with the existing BRB model and other methods in the literature. Experimental results demonstrate the capability of the proposed method for identification of nonlinear systems. Show more

Keywords: Belief rule base, system identification, evidence theory, weighted average, attribute reduction

DOI: 10.3233/IFS-162191

Citation: Journal of Intelligent & Fuzzy Systems, vol. 32, no. 6, pp. 3879-3891, 2017

Authors: Abdalla, Turki Y. | Abed, Ali A. | Ahmed, Alaa A.

Article Type: Research Article

Abstract: The objective of this paper is to develop a path planning algorithm that is able to plan the trajectory of mobile robots from its start point to target point in static and dynamic unknown environments. The classical artificial potential field (APF) method is not sufficient and ineffective for that purpose since it has the problem of local minima. To enhance the performance of the classical APF algorithm and to produce a more efficient and effective path planning for mobile robots, a new method based on combination of a modified APF algorithm with fuzzy logic (i.e. FAPF) is proposed. The proposed …algorithm is designed to overcome the problems of the classical APF especially the local minima and enhances the navigation in complex environments. The fuzzy logic controller (FLC) is also used for motion control of the mobile robot. The membership functions of the FLC are optimized with particle swarm optimization (PSO) algorithm for optimality. Simulation models for the proposed path planning and motion control methods are built with MATLAB. Simulation results are obtained and proved that the robot with FAPF navigates with smoother path, react much faster in static and dynamic environments, and avoid obstacles efficiently. The work is compared with other implementations that used conventional PID controllers. All the system is then implemented practically to prove the proposed algorithms and tested in complex and unknown environment. Show more

Keywords: Path planning, fuzzy logic, artificial potential field, fuzzy artificial potential field, PSO

DOI: 10.3233/IFS-162205

Citation: Journal of Intelligent & Fuzzy Systems, vol. 32, no. 6, pp. 3893-3908, 2017

Authors: Wu, Cheng | Song, Huichun | Yan, Changsheng | Wang, Yiming

Article Type: Research Article

Abstract: Reinforcement learning is hard to solve optimization problems in multi-agent system because of the inefficiency of function approximation. Sparse distributed memories, which is implemented using Radial Basis Functions or Kanerva Coding, can be used to improve the efficiency. But this approach still often gives poor performance when applied to large-scale multi-agent systems. In this paper, we attempt to solve four-rooms problem in the predator-prey pursuit domain and argue that the poor performance that we observe is caused by frequent prototype collisions. We show that dynamic prototype allocation and adaptation can give better results by reducing these collisions. By using our …novel approach about fuzzy Kanerva-based function approximation, that uses a fine-grained fuzzy membership grade to describe a state-action pair’s adjacency with respect to each prototype, we give some results that prototype collisions are completely eliminated and learning performance is greatly improved. We further show that prototype density varies widely across the state-action space and that this variation causes prototypes’ receptive fields to be unevenly distributed. This distribution limits the ability of fuzzy Kanerva Coding to achieve better results. It can be observed that fuzzy Kanerva Coding allows prototypes to adaptively tune their receptive fields for a target application. We conclude that fuzzy Kanerva Coding with prototype tuning and adaptation can significantly improve a reinforcement learner’s ability to solve large-scale multi-agent problems. Show more

Keywords: Reinforcement learning, function approximation, fuzzy system, pursuit problem

DOI: 10.3233/IFS-162212

Citation: Journal of Intelligent & Fuzzy Systems, vol. 32, no. 6, pp. 3909-3920, 2017