In this paper, we introduce the design of fuzzy respective space-based neuro-fuzzy networks for pattern recognition. The proposed networks are realized by partitioning of the fuzzy respective input space to generate the fuzzy rules. The respectively partitioned spaces using fuzzy respective input space express the rules of the networks. The consequence part of the rules is represented by polyno...

A fuzzified neural network copes with fuzzy signals and/or weights so that the information about the uncertainty of input and output can be served in the training process. Since learning process is the main function of fuzzy neural networks, in this study, we focus on review and comparison of the existing learning algorithms, so that the theoretical achievement and the application agenda of eac...

There exist various service systems that have hierarchical structure. In hierarchical service networks, facilities at different levels provide different types of services. For example, in health care systems, general centers provide low-level services such as primary health care services, while the specialized hospitals provide high-level services. Because of demand congestion in service networ...

The paper considers both knowledge acquisition and knowledge interpretation tasks as tightly connected and continuously interacting processes in a contemporary knowledge engineering system. Fuzzy rules are used here as a framework for knowledge representation. An algorithm REFuNN for fuzzy rules extraction from adaptive fuzzy neural networks (FuNN) is proposed. A case study of Iris classificati...

image classification is an issue which utilizes image processing, pattern recognition and classification methods. automatic medical image classification is a progressive area in image classification and it expected to be more developed in the future. due to this fact that automatic diagnosis which use intelligent methods such as medical image classification can assist pathologists by providing ...

Fuzzy neural networks can be trained with crisp and fuzzy data. J. Buckley and Y. Hayashi have shown that these networks are monotonic (see 2]) when extension principle based operations are used to compute the network output. In this paper we show that these networks are also overlapping. This property provides us with a means to theoretically analyse the output behaviour of fuzzy neural networ...

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...

With the advancement in wireless communication technology, various networks exists simultaneously. The variation in wireless network parameters is imprecise in nature. To handle this imprecise data fuzzy logic can be used as an important tool for wireless network algorithms. Application of fuzzy logic/fuzzy controller in wireless communications is presented in this paper. The objective of this ...

This paper investigates some properties of Takagi-Sugeno (T-S) fuzzy Hopfield neural networks. First, we prove that there exists a unique solution of the T-S fuzzy Hopfield neural network. Second, we determine a condition for input-to-state stability (ISS) of the T-S fuzzy Hopfield neural network. These results will be useful to analyze dynamic behavior of fuzzy neural networks.

Though traditional neural networks and fuzzy logic are powerful universal approximators, however without some refinements, they may not, in general, be good approximators for adaptive systems. By extending fuzzy sets to qualitative fuzzy sets, fuzzy logic may become universal approximators for adaptive systems. Similar considerations can be extended to neural networks.