scholarly journals Extracting T–S Fuzzy Models Using the Cuckoo Search Algorithm

2017 ◽  
Vol 2017 ◽  
pp. 1-9 ◽  
Author(s):  
Mourad Turki ◽  
Anis Sakly
Keyword(s):  
Nonlinear System ◽  
Search Algorithm ◽  
Nonlinear System Identification ◽  
Fuzzy Model ◽  
Cuckoo Search ◽  
Identification Problem ◽  
Cuckoo Search Algorithm ◽  
Modelling Problem ◽  
Plant Modelling ◽  
Takagi Sugeno

A new method called cuckoo search (CS) is used to extract and learn the Takagi–Sugeno (T–S) fuzzy model. In the proposed method, the particle or cuckoo of CS is formed by the structure of rules in terms of number and selected rules, the antecedent, and consequent parameters of the T–S fuzzy model. These parameters are learned simultaneously. The optimized T–S fuzzy model is validated by using three examples: the first a nonlinear plant modelling problem, the second a Box–Jenkins nonlinear system identification problem, and the third identification of nonlinear system, comparing the obtained results with other existing results of other methods. The proposed CS method gives an optimal T–S fuzzy model with fewer numbers of rules.

Constrained cuckoo search algorithm and Takagi-Sugeno fuzzy models for predictive control

2020 ◽  
pp. 014233122096630
Author(s):  
Adel Taeib ◽  
Hichem Salhi ◽  
Abdelkader Chaari
Keyword(s):  
Predictive Control ◽  
Search Algorithm ◽  
Fuzzy Model ◽  
Cuckoo Search ◽  
Cuckoo Search Algorithm ◽  
Optimization Stage ◽  
Predictive Controller ◽  
Action Taking ◽  
Particle Swarm Optimisation Algorithm ◽  
Takagi Sugeno

In this paper, a new predictive control scheme formulated by using the Takagi-Sugeno fuzzy modeling method and a new constrained cuckoo search algorithm. The cuckoo search algorithm is used to determine the predictive controls by minimizing a constrained criterion. The Takagi-Sugeno fuzzy modelling approach is applied to forecast the states of the process. At the optimization stage, the proposed cuckoo search provides the control action taking into account constraints. The performances of the developed method are tested during its application in the three-tank process. Therefore, the experimental results demonstrate that the combination of the philosophy of the fuzzy model and cuckoo search is very good in the controlling of nonlinear processes. In addition, the closed-loop performance of the developed method is compared to approach based with the particle swarm optimisation algorithm and those obtained with fuzzy model predictive controller.

Parameter identification of a reduced nonlinear model for an activated sludge process based on cuckoo search algorithm

2019 ◽  
Vol 41 (12) ◽  
pp. 3352-3363
Author(s):  
Taoufik Ladhari ◽  
Intissar Khoja ◽  
Faouzi Msahli ◽  
Anis Sakly
Keyword(s):  
Activated Sludge ◽  
Parameter Identification ◽  
Search Algorithm ◽  
Cuckoo Search ◽  
Identification Problem ◽  
Cuckoo Search Algorithm ◽  
Activated Sludge Process ◽  
Modeling And Control ◽  
Parameter Identification Problem ◽  
Nature Inspired Algorithms

Parameter identification plays a key role in systems’ modeling and control. This paper deals with a parameter identification problem for an activated sludge process used in wastewater treatment. The considered model is a nonlinear one inspired from the well-known ASM1. Nature-inspired algorithms have gained significant attention over the last years as useful means to solve parameter identification problem. The proposed approach in this paper is the cuckoo search algorithm based on both the fascinating brood parasitic behavior and the lévy flights. The advantages of this method are its simplicity and robustness, but it requires a good tuning of its parameters to have the best results. The comparison of the simulation results with the Nelder-Mead method, genetic algorithm, and particle swarm optimization proves the capability of this method to identify the model’s parameters with high precision.

Parameter estimation of Takagi–Sugeno fuzzy system using heterogeneous cuckoo search algorithm

2015 ◽  
Vol 151 ◽  
pp. 1332-1342 ◽  
Author(s):  
Xueming Ding ◽  
Zhenkai Xu ◽  
Ngaam J. Cheung ◽  
Xiaohui Liu
Keyword(s):  
Parameter Estimation ◽  
Fuzzy System ◽  
Search Algorithm ◽  
Cuckoo Search ◽  
Cuckoo Search Algorithm ◽  
Takagi Sugeno

Optimal broadcast scheduling method for VANETs: An adaptive discrete firefly approach

2020 ◽  
Vol 39 (6) ◽  
pp. 8125-8137
Author(s):  
Jackson J Christy ◽  
D Rekha ◽  
V Vijayakumar ◽  
Glaucio H.S. Carvalho
Keyword(s):  
Intelligent Transportation System ◽  
Search Algorithm ◽  
Mac Protocol ◽  
High Mobility ◽  
Optimal Solution ◽  
Cuckoo Search ◽  
Cuckoo Search Algorithm ◽  
Traffic Density ◽  
Scheduling Problem ◽  
Broadcast Scheduling

Vehicular Adhoc Networks (VANET) are thought-about as a mainstay in Intelligent Transportation System (ITS). For an efficient vehicular Adhoc network, broadcasting i.e. sharing a safety related message across all vehicles and infrastructure throughout the network is pivotal. Hence an efficient TDMA based MAC protocol for VANETs would serve the purpose of broadcast scheduling. At the same time, high mobility, influential traffic density, and an altering network topology makes it strenuous to form an efficient broadcast schedule. In this paper an evolutionary approach has been chosen to solve the broadcast scheduling problem in VANETs. The paper focusses on identifying an optimal solution with minimal TDMA frames and increased transmissions. These two parameters are the converging factor for the evolutionary algorithms employed. The proposed approach uses an Adaptive Discrete Firefly Algorithm (ADFA) for solving the Broadcast Scheduling Problem (BSP). The results are compared with traditional evolutionary approaches such as Genetic Algorithm and Cuckoo search algorithm. A mathematical analysis to find the probability of achieving a time slot is done using Markov Chain analysis.

scholarly journals Optimization strategy of wireless charger node deployment based on improved cuckoo search algorithm

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Yang Wang ◽  
Feifan Wang ◽  
Yujun Zhu ◽  
Yiyang Liu ◽  
Chuanxin Zhao
Keyword(s):  
Sensor Network ◽  
Sensor Node ◽  
Search Algorithm ◽  
Optimal Solution ◽  
Cuckoo Search ◽  
Cuckoo Search Algorithm ◽  
Sensor Nodes ◽  
Optimization Strategy ◽  
Multi Objective ◽  
Received Power

AbstractIn wireless rechargeable sensor network, the deployment of charger node directly affects the overall charging utility of sensor network. Aiming at this problem, this paper abstracts the charger deployment problem as a multi-objective optimization problem that maximizes the received power of sensor nodes and minimizes the number of charger nodes. First, a network model that maximizes the sensor node received power and minimizes the number of charger nodes is constructed. Second, an improved cuckoo search (ICS) algorithm is proposed. This algorithm is based on the traditional cuckoo search algorithm (CS) to redefine its step factor, and then use the mutation factor to change the nesting position of the host bird to update the bird’s nest position, and then use ICS to find the ones that maximize the received power of the sensor node and minimize the number of charger nodes optimal solution. Compared with the traditional cuckoo search algorithm and multi-objective particle swarm optimization algorithm, the simulation results show that the algorithm can effectively increase the receiving power of sensor nodes, reduce the number of charger nodes and find the optimal solution to meet the conditions, so as to maximize the network charging utility.

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