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...

The paper introduces an early-warning method using multiple financial crises indicators which outputs relevant alerts compared to a precise indication of crisis inception, serving as an effective tool for decision makers. By leveraging fuzzy logic techniques, we design a multi-level fuzzy decision support system based on the evolution of credit growth, housing prices and GDP gap. A neuro-fuzzy ...

In this paper a new identification method for non-linear hybrid systems that have mixed continuous and discrete states by using fuzzy clustering and principal component analysis is described. The method first determines the hybrid characteristic of the system inspired by an inverse form of the merge method for clusters, which makes it possible to identify the unknown switching points of a proce...

The systems represented by the Choquet or the multi-linear fuzzy integral with respect to fuzzy measure is equivalently decomposable into hierarchically sub-systems through the use of Inclusion-Exclusion Covering (IEC) (Theorem 4.1,4.2) . Hence, IEC is one of very useful concepts/indexes for structural analysis of the fuzzy integral systems (short for: the systems represented by the Choquet or ...

This research frame work investigates the application of a clustered based Neuro-fuzzy system to nonlinear dynamic system modeling from a set of input-output training patterns. It is concentrated on the modeling via Takagi-Sugeno (T-S) modeling technique and the employment of fuzzy clustering to generate suitable initial membership functions. Hence, such created initial memberships are then emp...

Generalized predictive control with flexible constraints is investigated. To apply fuzzy technique, the flexible parts in the inequality constraints are fuzzified as fuzzy constraints. The optimization problem of the generalized predictive control with flexible inequality constraints is converted into two sub-problems to get the lower and upper bound of the objective values, and then the object...

Companies, organizations, governmental departments, and universities need to adopt globalization patterns for their generative survival in dynamic productive outputs. Such outputs are possible only after effective, rational, logical, and systematic treatment of all available input knowledge and information. These inputs mostly have imprecision, uncertainty, vagueness, incompleteness, and missin...

This paper proposes the applications of soft computing to deal with the constraints in conventional modelling techniques of the dynamic extrusion process. The proposed technique increases the efficiency in utilising the available information during the model identification. The resultant model can be classified as a ‘grey-box model’ or has been termed as a ‘semi-physical model’ in the context. ...

Companies, organizations, governmental departments, and universities need to adopt globalization patterns for their generative survival in dynamic productive outputs. Such outputs are possible only after effective, rational, logical, and systematic treatment of all available input knowledge and information. These inputs mostly have imprecision, uncertainty, vagueness, incompleteness, and missin...

Companies, organizations, governmental departments, and universities need to adopt globalization patterns for their generative survival in dynamic productive outputs. Such outputs are possible only after effective, rational, logical, and systematic treatment of all available input knowledge and information. These inputs mostly have imprecision, uncertainty, vagueness, incompleteness, and missin...