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Design of neuro-controller for complex dynamic systems is a big challenge faced by the researchers. In this paper we present a design of a robust neuro-controller for a dynamic system to make the system response fast with no overshoot. Here the control action decided by the controller completely depends on the value of the error at that point of time. The position feedback which controls the bandwidth...
We propose a simplified architecture for a recurrent neural network designed for learning from structures. We describe the architecture and the implementation and show the performances of the net. Two examples from the science domain are discussed: the first uses a synthetic data set and the second illustrates a chemical problem. We discuss about the results and compare them to other applications,...
This paper presents a multi-agent system (MAS) for reduction of the bullwhip effect in fuzzy supply chains. First, it is shown that, even using an optimal ordering policy, without data sharing the bullwhip effect still exists in the supply chain. Then a multi-agent system is proposed to manage the bullwhip effect. The multi-agent system has four different types of agents. The multi-agent system applies...
Whether a rule-based fuzzy system has the ability to approximate any multivariate continuous function arbitrarily well is an important issue, especially for fuzzy control and fuzzy modeling. The answer to this issue concerning various Mamdani and Takagi-Sugeno (TS) fuzzy systems employing type-1 fuzzy sets is affirmative and well documented in the literature. As for type-2 (T2) fuzzy systems, the...
This paper shows an alternative method to find optimal solutions of a fuzzy linear programming (FLP) problems. The classical FLP problem is treated by using fuzzy restrictions in the form Ax les b where indicates a type-1 fuzzy set (T1 FS). The proposed approach uses joint A and b fuzzy parameters to solve a FLP model. Nonlinear membership functions are used to represent imprecisions in their parameters...
This paper presents an approach for time series forecasting using a new class of fuzzy neural networks called uninetworks. Uninetworks are constructed using a recent generalization of the classic and and or logic neurons. These generalized logic neurons, called unineurons, provide a mechanism to implement general nonlinear processing and introduce important characteristics of biological neurons such...
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