background and aims: depression disorder is one of the most common diseases, but the diagnosis is widely complicated and controversial because of interventions, overlapping and confusing nature of the disease. so, keeping previous patients’ profile seems effective for diagnosis and treatment of present patients. use of this memory is latent in synthetic neuro-fuzzy algorithm. present article in...

ABSTRACT: In this study, adaptive neuro-fuzzy inference system, and feed forward neural network as two artificial intelligence-based models along with conventional multiple linear regression model were used to predict the multi-station modelling of dissolve oxygen concentration at the downstream of Mathura City in India. The data used are dissolved oxygen, pH, biological oxygen demand and water...

Fuzzy logic, neural network, fuzzy-neural networks play an important role in the linguistic modeling of intelligent control and decision making in complex systems. The Fuzzy-Neural Network (FNN) learning represents one of the most effective algorithms to build such linguistic models. This paper proposes an Artificial Immune Algorithm (AIA) based optimal learning fuzzy-neural network (IM-FNN). T...

profitability as the most important factor in decision-making, has always been considered by stake­holders in the company's profitability. also can be a basis for evaluating the performance of the managers. the ability to predict the profitability can be very useful to help decision-makers. that's why one of the most important issues is the expected profitability. the importance of th...

In this paper, we present a new fuzzy reasoning method based on the compensating fuzzy reasoning (CFR). Its basicidea is to obtain a new fuzzy reasoning result by moving and deforming the consequent fuzzy set on the basis of themoving, deformation, and moving-deformation operations between the antecedent fuzzy set and observation information.Experimental results on real-world data sets show tha...

This paper presents a fuzzy system that recognizes learning styles and emotions using two different neural networks. The first neural network (a Kohonen neural network) recognizes the student cognitive style. The second neural network (a back-propagation neural network) was used to recognize the student emotion. Both neural networks are being part of a fuzzy system used into an intelligent tuto...

In order to overcome weaknesses of the conventional crisp neural network and the fuzzy-operation-oriented neural network, we have developed a general fuzzy-reasoning-oriented fuzzy neural network called a Crisp-Fuzzy Neural Network (CFNN) which is capable of extracting high-level knowledge such as fuzzy IF-THEN rules from either crisp data or fuzzy data. A CFNN can eeectively compress a 5 5 fuz...

This paper proposes ajuzzy neural expert system (FNES) with the following two functions: (1) Generalization of the information derived from the training data and embodiment of knowledge in the form of the fuzzy neural network; (2) Extraction of fuzzy If-Then rules with linguistic relative importance of each proposition in an antecedent (I f -part) from a trained neural network. This paper also ...

This paper proposes ajuzzy neural expert system (FNES) with the following two functions: (1) Generalization of the information derived from the training data and embodiment of knowledge in the form of the fuzzy neural network; (2) Extraction of fuzzy If-Then rules with linguistic relative importance of each proposition in an antecedent (I f -part) from a trained neural network. This paper also ...