This paper is a sequel to Kurano et al [9], [10], in which the fuzzy perceptive models for optimal stopping or discounted Markov decision process is given. We proposed a method of computing the corresponding fuzzy perceptive values. Here, we deal with the average case for Markov decision processes with fuzzy perceptive transition matrices and characterize the optimal average expected reward, ca...

This paper is a sequel to Kurano et al [9], [10], in which the fuzzy perceptive models for optimal stopping or discounted Markov decision process are proposed and the methods of computing the corresponding fuzzy perceptive values are given. Here, we deal with the average case for Markov decisin processes with fuzzy perceptive transition matrices and characterize the optimal average expected rew...

This paper applies different methods of map comparison to quantify the characteristics of three different land change models. The land change models used for simulation are termed as ―Stochastic Markov (St_Markov)‖, ―Cellular Automata Markov (CA_Markov)‖ and ―Multi Layer Perceptron Markov (MLP_Markov)‖ models. Various model validation techniques such as per category method, kappa statistics, co...

In order to improve the approximation property of the past fuzzy clustering algorithms when identifying systems, a fuzzy clustering neural network (FCNN) is proposed and is applied to conjunction speech recognition system. Based on the fuzzy system model, FCNN presents every state as a fuzzy system and uses continuous frames as the system input. With improving fuzzy clustering identification al...

investors use different approaches to select optimal portfolio. so, optimal investment choices according to return can be interpreted in different models. the traditional approach to allocate portfolio selection called a mean - variance explains. another approach is markov chain. markov chain is a random process without memory. this means that the conditional probability distribution of the nex...

This is the second paper in a series of two papers describing a novel approach for generalizing classical hidden Markov models using fuzzy measures and fuzzy integrals and their application to the problem of handwritten word recognition. This paper presents an application of the generalized hidden Markov models to handwritten word recognition. The system represents a word image as an ordered li...

A novel modeling technique for automatic control purposes is discussed. A fuzzy Markov system is proposed to describe both determined and random behavior of complex dynamic plants. The main advantage is its high computational speed. Another benefit of this method is its flexibility and applicability to both linear and nonlinear systems. A controlled Markov chain represents a fuzzy system with a...

Hori (né Uemura) et al. formulated the fuzzy-Bayes decision rule extension of the Wald decision function to fuzzy ORand AND-connectives with multiple subjective distributions. This decision rule maps and transforms a state of nature to fuzzy events. In this context, the map of subjective distributions is called the subjective possibility distribution and the map of utility functions is called t...

In this study we model the daily rainfall occurrence using Markov Chain Analogue Yearmodel (MCAYM) and the intensity or amount of daily rainfall using three different probability distributions; gamma, exponential and mixed exponential distributions. Combining the occurrence and intensity model we obtain Markov Chain Analogue Year gamma model (MCAYGM), Markov Chain Analogue Year exponentia...