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...

Hybrid intelligent systems combining fuzzy logic and neural networks are proving their effectiveness in a wide variety of real-world problems. Fuzzy logic and neural nets have particular computational properties that make them suited for particular problems and not for others. For example, while neural networks are good at recognizing patterns, they are not good at explaining how they reach the...

The presentation is focused on comparison of neural networks and fuzzy systems. Advantages and disadvantages of both technologies are discussed. Fuzzy systems are relatively easy to design but number of inputs in the system are significantly limited. It is very difficult to design neural networks so rather they have to be trained instead. Neural networks produce much smoother nonlinear mapping ...

We examine various and different approaches for the prediction of economic crisis periods of US economy. We examine the traditional econometric discrete choice Logit and Probit models then a feed-forward neural network (FFNN) model and finally we apply an Adaptive Neuro-Fuzzy Inference System (ANFIS). We examine the period 1950-2009, where we take as the in-sample or training period 1950-2005, ...

semiarid regions with their exceptional weather conditions, low precipitation, and high evapotranspiration pose a great challenge to water resources managers. one possible way to face this challenge is the conjunctive use of both surface water and groundwater resources in these regions. this paper proposes a conjunctive use model which has been implemented in najafabad plain in central iran. th...

In this contribution a new supervised classification model is proposed, namely the Fuzzy Evolutionary Probabilistic Neural Network (FEPNN). The proposed model incorporates a fuzzy class membership function into the recently proposed Evolutionary Probabilistic Neural Network (EPNN). EPNN employs an evolutionary algorithm, namely the Particle Swarm Optimization (PSO), for the selection of the spr...

In recent years, soft computing methods, like fuzzy logic and neural networks have been  presented and developed for the purpose of mobile robot trajectory tracking. In this paper we will present a fuzzy approach to the problem of mobile robot path tracking for the CEDRA rescue robot with a complicated kinematical model. After designing the fuzzy tracking controller, the membership functions an...

Traditional air quality assessment is realized using air quality indices which are determined as mean values of selected air pollutants. Thus, air quality assessment depends on strictly given limits without taking into account specific local conditions and synergic relations between air pollutants and other meteorological factors. The stated limitations can be eliminated, e.g. using systems bas...