As we all know, the parameter optimization of Mamdani model has a defect of easily falling into local optimum. To solve this problem, we propose a new algorithm by constructing Mamdani Fuzzy neural networks. This new scheme uses fuzzy clustering based on particle swarm optimization (PSO) algorithm to determine initial parameter of Mamdani Fuzzy neural networks. Then it adopts PSO algorithm to o...

Localization is one of the most important research topics in the wireless sensor network applications. To improve the indoor localization accuracy, the centroid localization algorithm based on Mamdani fuzzy system has been adopted to attain the weight between sensor node and anchor node. This paper proposes a novel optimized input membership function by bat algorithm in fuzzy inference system u...

This paper presents a novel hybridization of the fuzzy logic, the neural network and the coevolutionary algorithm for building a fuzzy-neural system (or a Mamdani fuzzy system) from data. The novel hybridization uses the coevolution of many species, and proposes the coevolution of groups of similar species, both for the optimization of the structure of the fuzzy-neural network. In the fuzzy-neu...

Hybrid algorithm is the hot issue in Computational Intelligence (CI) study. From in-depth discussion on Simulation Mechanism Based (SMB) classification method and composite patterns, this paper presents the Mamdani model based Adaptive Neural Fuzzy Inference System (M-ANFIS) and weight updating formula in consideration with qualitative representation of inference consequent parts in fuzzy neura...

The Chiu’s method which generates a Takagi-Sugeno Fuzzy Inference System (FIS) is a method of fuzzy rules extraction. The rules output is a linear function of inputs. In addition, these rules are not explicit for the expert. In this paper, we develop a method which generates Mamdani FIS, where the rules output is fuzzy. The method proceeds in two steps: first, it uses the subtractive clustering...

The Chiu’s method which generates a Takagi-Sugeno Fuzzy Inference System (FIS) is a method of fuzzy rules extraction. The rules output is a linear function of inputs. Those rules are not explicit for the expert. This paper proposes a new method to generate Mamdani FIS, where the rules output is fuzzy. The method proceeds in two steps. The first step consists in using the subtractive clustering ...

An explainable artificial intelligence (XAI) agent is an autonomous that uses a fundamental XAI model at its core to perceive environment and suggests actions be performed. One of the significant challenges for these agents performing their operation efficiently, which governed by underlying inference optimization system. Along similar lines, Explainable Fuzzy AI Challenge (XFC 2022) competitio...