there are different methods of reconstructing hydrologic data. depending on the conditions of the station a particular method can produce the best results. generally, in order to estimate the lost data in a station and its surrounding stations, hydrologic, climatologic and/or physiolographic similarities are used. recently, the fuzzy regression method has been used to reconstract the hydrologic...

fuzzy cognitive maps (fcms) have successfully been applied in numerous domains to show the relations between essential components in complex systems. in this paper, a novel learning method is proposed to construct fcms based on historical data and by using meta-heuristic: genetic algorithm (ga), simulated annealing (sa), and tabu search (ts). implementation of the proposed method has demonstrat...

In real decision situations a decision maker (DM) is often confronted with the problem that the information which is necessary for constructing a classical decision model is not available, or the cost for getting this information seems too high. Subsequently, the DM abstains from constructing a decision model; the DM fears that this model is not an authentic image of the real problem. Fuzzy set...

The translation of knowledge contained in databank into linguistically interpretable fuzzy rules has proven in real applications to be difficult. A solution to this problem is furnished by multiresolution techniques. A dictionary of functions forming a multiresolution is used as candidate membership functions. The membership functions are chosen among the family of scaling functions that have t...

Although data mining is relative young technique, it has been used in a wide range of problem domains over the past few decades. In this paper, the authors present a new model to forecast the cultivated land demand adopts the technique of data mining. The new model which is called fuzzy Markov Chain model with weights ameliorate the traditional Time Homogeneous Finite Markov chain model to pred...

A neuro-fuzzy system is the combined the advance feature of fuzzy logic and neural network, it is simply a fuzzy inference system that is trained by the learning concept of neural network. In NFS learning mechanism fine-tunes the underlying fuzzy inference system. This paper presents fundamental concepts and parameterized comparison in the aspects of fuzzy logic, neural network and neuro-fuzzy ...

This paper presents the simulation results for the fuzzy control of slow processes with or without dead time. Many results obtained by a lot of crisp and fuzzy control techniques for the error gain and (or) variation error in connection with the transient performances (settling time, rise time, overshoot etc.) are reported in the specialty literature. The fuzzy control techniques for the error ...

Prediction, diagnosis, recovery and recurrence of the breast cancer among the patients are always one of the most important challenges for explorers and scientists. Nowadays by using of the bioinformatics sciences, these challenges can be eliminated by using of the previous information of patients records. In this paper has been used adaptive nero fuzzy inference system and data mining techniqu...

A combined approach to data-driven fuzzy rule-based modeling is described. The rules of an initial model are derived from data by means of a supervised clustering method that to a certain degree ensures the transparency of the resulting rule base. This model is, however, suboptimal, and a realcoded genetic algorithm (GA) is proposed to optimize simultaneously both the antecedent and the consequ...