Monotonic type-2 fuzzy neural network and its application to thermal comfort prediction

Li, Chengdong; Yi, Jianqiang; Wang, Ming; Zhang, Guiqing

doi:10.1007/s00521-012-1140-x

Cited by 26 publications

(7 citation statements)

References 33 publications

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“…Similarly, when the situation is so fuzzy that we have trouble determining the membership grade even as a crisp number in [0, 1], we use fuzzy sets of type-2 (cf. Li et al [28]). …”

Section: Type-2 Fuzzy Setsmentioning

confidence: 99%

Mean and CV reduction methods on Gaussian type-2 fuzzy set and its application to a multilevel profit transportation problem in a two-stage supply chain network

Jana

Pramanik

Maiti

2016

Neural Comput & Applic

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The transportation problem (TP) is an important supply chain optimization problem in the traffic engineering. This paper maximizes the total profit over a three-tiered distribution system consisting of plants, distribution centers (DCs) and customers. Plants produce multiple products that are shipped to DCs. If a DC is used, then a fixed cost (FC) is charged. The customers are supplied by a single DC. To characterize the uncertainty in the practical decision environment, this paper considers the unit cost of TP, FC, the supply capacities and demands as Gaussian type-2 fuzzy variables. To give a modeling framework for optimization problems with multifold uncertainty, different reduction methods were proposed to transform a Gaussian type-2 fuzzy variable into a type-1 fuzzy variable by mean reduction method and CV reduction method. Then, the TP was reformulated as a chanceconstrained programming model enlightened by the credibility optimization methods. The deterministic models are then solved using two different soft computing techniques-generalized reduced gradient and modified particle swarm optimization, where the position of each particle is adjusted according to its own experience and that of its neighbors. The numerical experiments illustrated the application and effectiveness of the proposed approaches.

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“…Similarly, when the situation is so fuzzy that we have trouble determining the membership grade even as a crisp number in [0, 1], we use fuzzy sets of type-2 (cf. Li et al [28]). …”

Section: Type-2 Fuzzy Setsmentioning

confidence: 99%

Mean and CV reduction methods on Gaussian type-2 fuzzy set and its application to a multilevel profit transportation problem in a two-stage supply chain network

Jana

Pramanik

Maiti

2016

Neural Comput & Applic

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“…These fuzzy rules can be further verified via the monotonicity. 17,18 The inference engine is activated by the linguistic rules of the fuzzy control algorithm in Table 1. If ½ s, D s lies on the diagonal of Table 1 (i.e.…”

Section: Dfsmc Designmentioning

confidence: 99%

Digital fuzzy sliding-mode control for a linear parameter-varying air–fuel ratio system

Tafreshi

2019

Advances in Mechanical Engineering

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Air-fuel ratio is a key factor for the minimization of the harmful pollutant emissions and maximization of fuel economy. However, a big challenge for air-fuel ratio control is a large time-varying delay existing in spark ignition engines. In this article, a digital fuzzy sliding-mode controller is proposed to control a linear parameter-varying sampled-data air-fuel ratio system. First, the Pade first-order technique is utilized to approximate the time-varying delay. The resultant system-a linear parameter-varying continuous-time air-fuel ratio system with unstable internal dynamics-is then discretized to a linear parameter-varying sampled-data air-fuel ratio system appropriate for a discrete-time control approach. Based on the linear parameter-varying sampled-data air-fuel ratio system, a stable sliding surface with a desired tracking error dynamics is presented. Two input scaling factors and one output scaling factor are determined for the proposed digital fuzzy sliding-mode controller. Then, the fuzzy inference is executed through a look-up table to stabilize the sliding surface into a convex set, and then make the tracking error possess uniformly ultimately bounded performance. The overall system stability is verified by Lyapunov's stability criteria. Finally, the simulation results demonstrate the feasibility, effectiveness, and robustness of the proposed control scheme under different operating conditions and show the superiority of the proposed approach performance compared to the baseline controller.

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“…Deng et al [36] handle monotonicity of TS fuzzy systems for classification on a set of virtual samples expressed as constraints on related optimization tasks. Applications of monotonic fuzzy systems can be found in the detection of failures [37], decision making [38], thermal comfort prediction [39], assessing material recyclability [40] and classification [9,41,42].…”

Section: Introductionmentioning

confidence: 99%

Monotonic Fuzzy Systems With Goniometric Membership Functions

Hušek

2024

Int. J. Fuzzy Syst.

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Fuzzy logic-based systems are nowadays commonly used in nonlinear function approximation when incoming data are available. Their main advantage is that the resulting rules can be interpreted understandably. Nevertheless, when the data are noisy an overfitting may occur which leads to poor accuracy and generalization ability. Prior information about the nonlinear function may improve fuzzy system performance. In this paper the case when the function is monotonic with respect to some or all variables is considered. Sufficient conditions for the monotonicity of first-order Takagi–Sugeno fuzzy systems with raised cosine membership functions are derived. Performance of the proposed fuzzy system is tested on two benchmark datasets

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Monotonic type-2 fuzzy neural network and its application to thermal comfort prediction

Cited by 26 publications

References 33 publications

Mean and CV reduction methods on Gaussian type-2 fuzzy set and its application to a multilevel profit transportation problem in a two-stage supply chain network

Mean and CV reduction methods on Gaussian type-2 fuzzy set and its application to a multilevel profit transportation problem in a two-stage supply chain network

Digital fuzzy sliding-mode control for a linear parameter-varying air–fuel ratio system

Monotonic Fuzzy Systems With Goniometric Membership Functions

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