Design of type-2 Fuzzy Logic Systems Based on Improved Ant Colony Optimization

Zhang, Zhifeng; Wang, Tao; Chen, Yang; Lan, Jie

doi:10.1007/s12555-017-0451-1

Cited by 12 publications

(9 citation statements)

References 14 publications

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“…In the following, some of the latest research in this field will be reviewed and analyzed. In (Zhang et al 2019 ), a general T2F-NN has been used for Mackey–Glass time series data (for τ = 17) prediction. The disadvantage of the mentioned paper is the training time of their proposed general T2F-NN, because it trains and predicts to the work of others over a longer period of time.…”

Section: T2f-nnsmentioning

confidence: 99%

A review on type-2 fuzzy neural networks for system identification

2021

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In many engineering problems, the systems dynamics are uncertain, and then, the accurate dynamic modeling is required. Type-2 fuzzy neural networks (T2F-NNs) are extensively used in system identification problems, because of their strong estimation capability. In this paper, the application of T2F-NNs is reviewed and classified. First, an introduction to the principles of system identification, including how to extract data from a system, persistency of excitation, preprocessing of information and data, removal of outlier data, and sorting of data to learn the T2F-NNs, is presented. Then, various learning methods for structure and parameters of the T2F-NNs are reviewed and analyzed. A number of different T2F-NNs that have been used to system identification are reviewed, and their disadvantages and advantages are described. Also, their efficiency in different applications is reviewed. Finally, we will look at the horizon ahead in this issue and analyze its challenges.

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Section: T2f-nnsmentioning

confidence: 99%

A review on type-2 fuzzy neural networks for system identification

2021

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“…Each order has a final delivery date constraint (the same distributor may have different delivery dates for the two products A and B). Contact with process providers to obtain process-related data such as total production time and total production cost for each order at each process provider [38,39]. The time required for each process workshop of the core manufacturer to complete each process with the co-manufacturer is shown in Tab.…”

Section: Instance Datamentioning

confidence: 99%

Supply Chain Joint Inventory Management and Cost Optimization Based on Ant Colony Algorithm and Fuzzy Model

Hou

Xia

et al. 2019

Teh. vjesn.

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With the advancement of the marketization process, inventory management has transformed from a single backup protection function to an essential function for enterprises, which helps to survive and develop. Inventory control in supply chain management is the important content of supply chain management. The new management mode makes inventory management present many new characteristics and problems compared with traditional inventory management. From the view of system theory and integration theory, it is imperative to reexamine the problem of inventory control, put forward new inventory management strategies adapted to integrated supply chain management, and improve the integration of the whole supply chain, which can enhance the agility and market response speed of enterprises. Based on the in-depth study of the joint inventory management model, this paper analyzed the current situation of the joint inventory management to optimize the inventory. In view of the achievements and shortcomings of the current research, a more systematic and improved optimization model of the supply chain inventory was proposed by using the basic ideas of ant colony algorithm and fuzzy model.

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“…For mobile robot path planning, the convergence of ACO is more sensitive to the initial parameter settings [20]. In response to the shortcomings, Shan [14]and Ou [11] et al improved the global search capability by combining the pheromone update with a differential evolutionary algorithm and improved selection of ant colony nodes, respectively.…”

Section: Introductionmentioning

confidence: 99%

“…In response to the shortcomings, Shan [14]and Ou [11] et al improved the global search capability by combining the pheromone update with a differential evolutionary algorithm and improved selection of ant colony nodes, respectively. Zhang [20] et al accelerated the convergence of ACO by updating the path pheromone of the optimal ant in each generation, which makes the ant colony easier to approach the optimal solution in a complex environment. Ali [1] et al proposed a twostage ant colony algorithm for the problem that the algorithm is prone to fall into locally optimum solutions.…”

Section: Introductionmentioning

confidence: 99%

Path Planning for Mobile Robots Based on an Improved Ant Colony Algorithm with Gaussian Distribution

Liu

Song

et al. 2022

J. Phys.: Conf. Ser.

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Optimal path planning for mobile robots in complex environments has the problem of tangential collision with obstacles. This paper proposed an ant colony algorithm based on Gaussian distributed pheromone volatile mechanism (GD-ACO). The algorithm can reduce the collision probability with obstacles during the search navigation, and it increased the search efficiency and avoidance capability of mobile robot in complex environment path planning. Firstly, we established a grid environment model and introduced a Gaussian distribution pheromone volatility mechanism to perform optimal path planning. Secondly, we introduced right triangle constraints to eliminate the unqualified paths and reduce the complexity of the algorithm through a pheromone reward mechanism. Finally, by improving the heuristic information function distribution of the ant colony algorithm, it solved the algorithm cannot effectively converge to the optimal solution when encountering special obstacle distribution and achieved more efficient obstacle avoidance. Compared with other path planning algorithms, it can effectively generate a collision-free path. The simulation experiments show the improved ant colony algorithm has better obstacle avoidance capability and faster performance, and the proposed method can effectively find the optimal collision-free path through experimental verification.

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Design of type-2 Fuzzy Logic Systems Based on Improved Ant Colony Optimization

Cited by 12 publications

References 14 publications

A review on type-2 fuzzy neural networks for system identification

A review on type-2 fuzzy neural networks for system identification

Supply Chain Joint Inventory Management and Cost Optimization Based on Ant Colony Algorithm and Fuzzy Model

Path Planning for Mobile Robots Based on an Improved Ant Colony Algorithm with Gaussian Distribution

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