Soft computing is a general term for algorithms that learn from human knowledge and mimic human skills. Example of such algorithms are fuzzy inference systems and neural networks. Many applications, especially in control engineering , have demonstrated their appropriate-ness in building intelligent systems that are exible and robust. Although recent research have shown that certain class of neu...

Neural and fuzzy courses are widely offered at graduate and undergraduate level due to the successful applications of neural and fuzzy control to nonlinear and unmodeled dynamic systems, including electrical drives. However, teaching students a neurofuzzy controlled electrical drive in a laboratory environment is often difficult for schools with limited access to expensive equipment facilities....

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...

Fault diagnosis of pneumatic system of automatic production line is studied in this paper. A fuzzy neural network fault diagnosis expert system and pneumatic circuit fault diagnosis instrument are designed. The experiment device is built as well. In the end some experiments are done, whose results show that the expert system using fuzzy neural network can diagnose fast and truly fault of pneuma...

Function Points is an important and well-accepted software size metric. However, it is absolutely essential to accurately calibrate Function Point (FP), whose aims are to fit specific software application, to reflect software industry trend, and to improve cost estimation. Neuro-Fuzzy is a technique that incorporates the learning ability from neural network and the ability to capture human know...

This paper addresses the fault detection of a cracked cantilever beam using a hybrid artificial intelligence technique. The hybrid technique used here uses a fuzzy-neuro controller. The fuzzy-neuro controller has two parts. The first part is comprised of the fuzzy controller, and the second part is comprised of the neural controller. The input parameters of the fuzzy controller are relative dev...

A novel fuzzy-neural tree (FNT) is presented, where each tree node uses a Gaussian as a fuzzy membership or possibility distribution in place of sigmoidal function in conventional neural networks. Although neural networks with Gaussian activation functions as well as different types of cooperative neuro-fuzzy systems have been extensively described in the literature, the FNT presented in this p...

This paper provides an overview on the contribution of soft computing to the field of behavior based robotics. It discusses the role of pure fuzzy, neuro-fuzzy and genetic fuzzy rule-based systems for behavior architectures and adaptation. It reviews a number of applications of soft computing techniques to autonomous robot navigation and control.

Fuzzy data analysis as we interpret it in this paper is the application of fuzzy systems to the analysis of crisp data. In this area, neuro-fuzzy systems play a very prominent role and are applied to a variety of data analysis problems like classification, function approximation or time series prediction. Fuzzy data analysis in general and neuro-fuzzy methods in particular make it easy to strik...

Short term prediction of traffic flow is one of the most essential elements of all proactive traffic control systems. Although various methodologies have been applied to forecast traffic parameters, several researchers have showed that compared with the individual methods, hybrid methods provide more accurate results . These results made the hybrid tools and approaches a more common method for ...