to use soil hydrology processes (shp) models, which have increasingly extended during the last years, comprehensive knowledge about these models and their modeling approaches seems to be necessary. the modeling approaches can be categorized as either classical or non-classical. classical approaches mainly model the shp through solving the general unsaturated flow (richards) equation, numericall...

In this paper, we investigate a generalized model of fuzzy cellular neural networks with distributed delays and impulses. By employing the theory of topological degree, M -matrix and Lypunov functional, we find sufficient conditions for the existence, uniqueness and global exponential stability of both the equilibrium point and the periodic solution. Two examples are given to illustrate the res...

The paper presents a general framework of connectionistbased, intelligent decision support systems and its realisation with the use of fuzzy neural networks FuNNs and evolving fuzzy neural networks EFuNNs. FuNNs and EFuNNs facilitate learning from data, fuzzy rule insertion, rule extraction, and adaptation. Several applications of this framework on real problems are presented as case studies, t...

Although CO2 injection is one of the most common methods in enhanced oil recovery, it could alter fluid properties of oil and cause some problems such as asphaltene precipitation. The maximum amount of asphaltene precipitation occurs near the fluid pressure and concentration saturation. According to the description of asphaltene deposition onset, the bubble point pressure has a very special imp...

Hierarchical fuzzy neural networks can use less rules to model nonlinear system with high accuracy. But the normal training method for hierarchical fuzzy neural networks is very complex. In this paper we modify the backpropagation approach and employ a time-varying learning nte that is determined from input-output data and model stnicture. Stable learning algorithms for the premise and the cons...

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...

Fuzzy controllers are designed to work with knowledge in the form of linguistic control rules. But the translation of these linguistic rules into the framework of fuzzy set theory depends on the choice of certain parameters, for which no formal method is known. The optimization of these parameters can be carried out by neural networks, which are designed to learn from training data, but which a...

This paper reviews neuro-fuzzy systems, which combine methods from neural network theory with fuzzy systems. Such combinations have been considered for several years already. However, the term neuro-fuzzy still lacks proper deenition, and still has the avour of a buzzword to it. Surprisingly few neuro-fuzzy approaches do actually employ neural networks, even though they are very often depicted ...

In this paper, a new architecture combining dynamic neural units and fuzzy logic approaches is proposed for a complex chemical process modeling. Such processes need a particular care where the designer constructs the neural network, the fuzzy and the fuzzy neural network models which are very useful in black box modeling. The proposed architecture is specified to the pH chemical reactor due to ...

Recently, artificial neural networks (ANNs) have been extensively studied and used in different areas such as pattern recognition, associative memory, combinatorial optimization, etc. In this paper, we investigate the ability of fuzzy neural networks to approximate solution of a dual fuzzy polynomial of the form a1x+ ...+anx n = b1x+ ...+ bnx n+d, where aj , bj , d ε E 1 (for j = 1, ..., n). Si...