Using neural network we try to model the unknown function f for given input-output data pairs. The connection strength of each neuron is updated through learning. Repeated simulations of crisp neural network produce different values of weight factors that are directly affected by the change of different parameters. We propose the idea that for each neuron in the network, we can obtain quasi-fuz...

A retrospective evaluation of attitudinal, behavioural and knowledge change among diverse stakeholder groups was conducted in Limpopo Province of South Africa to assess the effectiveness of a series of values clarification (VC) interventions. Telephone and face-to-face interview (193) results revealed that over two-thirds (70.2%) reported behavioural changes and 93.2% reported increased compass...

Fuzzy clustering is capable of finding vague boundaries that crisp clustering fails to obtain. But time complexity of fuzzy clustering is usually high, and the need to specify complicated parameters hinders its use. In this paper, an entropy-based fuzzy clustering method is proposed. It automatically identifies the number and initial locations of cluster centers. It calculates the entropy at ea...

in this paper, a novel hybrid method based on learning algorithmof fuzzy neural network and newton-cotesmethods with positive coefficient for the solution of linear fredholm integro-differential equation of the second kindwith fuzzy initial value is presented. here neural network isconsidered as a part of large field called neural computing orsoft computing. we propose alearning algorithm from ...

This paper extends a comparison measure called the statisfaction function(SF). The SF estimates the degree to which arithmetic comparisons between two fuzzy values are satissed. The previously proposed SF was deened on a discrete domain(SFD). So, in order to compare continuous fuzzy values, the fuzzy values should be converted into discrete ones. This paper deenes a satisfaction function on a c...

The choice of membership functions plays an essential role in the success of fuzzy systems. This is a complex problem due to the possible lack of knowledge when assigning punctual values as membership degrees. To face this handicap, we propose a methodology called Ignorance functions based Interval-Valued Fuzzy Decision Tree with genetic tuning, IIVFDT for short, which allows to improve the per...

This paper expresses the prominent features of the fuzzy expert system by applying the algorithm Fuzzy Assessment Methodology using K ratio. To diagnosis the diabetes Fuzzy Assessment Methodology using k ratio is developed. Fuzzy Expert System consists of following elements such as Fuzzification interface, Fuzzy Assessment Methodology using K ratio and Defuzzification interface. The crisp value...