Nonlinear inter-symbol interference leads to significant error rate in nonlinear communication and digital storage channel. In this paper, therefore, a novel recurrent interval type-2 fuzzy neural network with asymmetric membership functions (RT2FNN-A) is proposed for nonlinear channel equalization. The RT2FNN-A uses the interval asymmetric type-2 fuzzy sets and it implements the fuzzy logic sy...

Neuro-fuzzy systems-the combination of artiicial neural networks with fuzzy logic-are becoming increasingly popular. However, neuro-fuzzy systems need to be extended for applications which require context (e.g., speech, handwriting, control). Some of these applications can be modeled in the form of nite-state automata. Previously, it was proved that deterministic nite-state automata (DFAs) can ...

In this paper, we propose a novel recurrent interval type-2 fuzzy neural network with asymmetric membership functions (RT2FNN-A). The RT2FNN-A uses the interval asymmetric type-2 fuzzy sets and it implements the fuzzy logic system (FLS) in a five-layer neural network structure. The RT2FNN-A is modified from the type-2 fuzzy neural network to provide memory elements for capturing the system’s dy...

This paper intends to offer a new iterative method based on articial neural networks for finding solution of a fuzzy equations system. Our proposed fuzzied neural network is a ve-layer feedback neural network that corresponding connection weights to output layer are fuzzy numbers. This architecture of articial neural networks, can get a real input vector and calculates its corresponding fuzzy o...

In this paper we present a recurrent neural network model to recognize efficient Decision Making Units(DMUs) in Data Envelopment Analysis(DEA). The proposed neural network model is derived from an unconstrained minimization problem. In theoretical aspect, it is shown that the proposed neural network is stable in the sense of lyapunov and globally convergent. The proposed model has a single-laye...

Associative memories (AMs) are mathematical models inspired by the human brain ability to store and recall information. This paper introduces the fuzzy exponential recurrent neural networks (FERNNs), which can implement an AM for the storage and recall of fuzzy sets. The novel models are obtained by modifying the multivalued exponential recurrent neural network of Chiueh and Tsai. Briefly, a FE...

correct estimation of suspended sediment transported by a river is an important practice in water structure design, environmental problems and water quality issues. conventionally, sediment rating curve used for suspended sediment estimation in rivers. in this method discharge and sediment discharge or concentration related using regression relation that generally is exponential model. respect ...

In this paper, an output based adaptive iterative learning controller using an output recurrent fuzzy neural network is proposed for a class of uncertain nonaffine nonlinear systems. It is assumed that the states are not measurable. Without state observer, a sliding window of measurement is introduced to design the iterative learning controller. The main structure of this controller is construc...

awareness of the level of river flow and its fluctuations at different times is one of the significant factor to achieve sustainable development for water resource issues. therefore, the present study two hybrid models, wavelet- adaptive neural fuzzy interference system (wanfis) and wavelet- artificial neural network (wann) are used for flow prediction of gamasyab river (nahavand, hamedan, iran...

In this paper, global robust stability of stochastic impulsive recurrent neural networks with time-varyingdelays which are represented by the Takagi-Sugeno (T-S) fuzzy models is considered. A novel Linear Matrix Inequality (LMI)-based stability criterion is obtained by using Lyapunov functional theory to guarantee the asymptotic stability of uncertain fuzzy stochastic impulsive recurrent neural...