In this paper, We interpret a fuzzy differential equation by using the strongly generalized differentiability concept. Utilizing the Generalized characterization Theorem. Then a novel hybrid method based on learning algorithm of fuzzy neural network for the solution of differential equation with fuzzy initial value is presented. Here neural network is considered as a part of large field called ...

This paper presents a pseudo-equivalence of the digital PI fuzzy controllers with the PI linear controllers in the continuous time domain. A method to design PI fuzzy controller is presented. Transfer functions and equivalence relations between controller’s parameters are obtained for the common structures of the PI fuzzy controllers. The pseudo-equivalence is made based on a grapho-analithic a...

Spatial data is quite often is prone to uncertainty and imprecision. For this purpose, fuzzy regions have been developed: they basically consist of a fuzzy set over a two dimensional domain, allowing for both fuzzy regions and fuzzy points to be modelled. The model is extended to a level-2 fuzzy region to overcome some limitations, but this has an impact on operations. In this contribution, we ...

Neuro{fuzzy combination are considered for several years already. However, the term \neuro{fuzzy" still lacks of proper deenition, and it has the avor of a \buzz word". In this paper we try to give it a meaning in the context of fuzzy classiication systems. From our point of view \neuro{fuzzy" means the employment of heuristic learning strategies derived from the domain of neural network theory...

| Coevolutionary algorithms have received increased attention in the past few years within the domain of evolutionary computation. In this paper, we combine the search power of coevolutionary computation with the expressive power of fuzzy systems, introducing a novel algorithm , Fuzzy CoCo: Fuzzy Cooperative Coevolution. We demonstrate the eecacy of Fuzzy CoCo by applying it to a hard, real-wor...

Coevolutionary algorithms have received increased attention in the past few years within the domain of evolutionary computation. In this paper, we combine the search power of coevolutionary computation with the expressive power of fuzzy systems, introducing a novel algorithm, Fuzzy CoCo: Fuzzy Cooperative Coevolution . We demonstrate the efficacy of Fuzzy CoCo by applying it to a hard, real-wor...

Removing the condition of symmetry in the notion of a fuzzy (pseudo)metric, in Kramosil and Michalek’s sense, one has the notion of a fuzzy quasi-(pseudo-)metric. Then for each fuzzy quasi-pseudo-metric on a set X we construct a fuzzy quasipseudo-metric on the collection of all nonempty subsets of X, called the Hausdorff fuzzy quasi-pseudo-metric. We investigate several properties of this struc...

Let X be a dcpo and let L be a complete lattice. The family σL(X) of all Scott continuous mappings from X to L is a complete lattice under pointwise order, we call it the L-fuzzy Scott structure on X. Let E be a dcpo. A mapping g : σL(E) −> M is called an LM-fuzzy possibility valuation of E if it preserves arbitrary unions. Denote by πLM(E) the set of all LM-fuzzy possibility valuations of E. T...

This paper studies the iterative learning control of robotic systems with repetitive tasks. A fuzzy neural network is applied to design a direct adaptive iterative learning controller. The fuzzy neural network is introduced for compensation of the unknown certainty equivalent controller. A new adaptive law using mixed time-domain and iteration-domain adaptation is developed. It is shown that th...

A framework of new unified neural and neuro-fuzzy approaches for integrating implicit and explicit knowledge in neuro-symbolic systems is proposed. In the developed hybrid system, training data set is used for building neurofuzzy modules, and represents implicit domain knowledge. On the other hand, the explicit domain knowledge is represented by fuzzy rules, which are directly mapped into equiv...