Adaptive control of nonlinear systems has been an active area in recent years, but it is difficult to control unknown plants. A common approach to deal with this problem is to utilize the simultaneous identification technique. Neural networks have been employed in the identification and control of unknown nonlinear systems owing to their massive parallelism, fast adaptation and learning capabil...

this study investigates the prediction model of compressive strength of self–compacting concrete (scc) by utilizing soft computing techniques. the techniques consist of adaptive neuro–based fuzzy inference system (anfis), artificial neural network (ann) and the hybrid of particle swarm optimization with passive congregation (psopc) and anfis called psopc–anfis. their performances are comparativ...

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 ...

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 ...

Drought forecasting plays an important role in the planning and management of water resources systems. In this paper, a hybrid wavelet and adaptive neuro-fuzzy inference system (WANFIS) is proposed for drought forecasting. The WANFIS model was developed by combining two methods, namely a discrete wavelet transform and adaptive neuro-fuzzy inference system (ANFIS) model. To assess the effectiven...

using current employee performance data to predict the future behavior of the applicants is an interesting area which can broaden new horizons of knowledge lay in the organization. because of inherent ambiguity and uncertainty, cognitive limitations of the human mind make unknown behaviors of very complex systems difficult to predict. as a consequence, it is necessary to model the imprecise mod...

A neuro-fuzzy system specially suited for efficient implementations is presented. The system is of the same type as the well-known “adaptive network-based fuzzy inference system” (ANFIS) method. However, different restrictions are applied to the system that considerably reduce the complexity of the inference mechanism. Hence, efficient implementations can be developed. Some experiments are pres...

This paper presents a hybrid filter for denoising and enhancing digital image in situation where the image is corrupted by salt and pepper noise. Image denoising and enhancement are important preprocessing and post processing steps in image analysis. Successful results of image analysis extremely depend on image enhancement. There are several filters have been illustrated till date. But they ar...

A modular analogue neuro-chip set with on-chip learning capability is developed for active noise canceling. The analogue neuro-chip set incorporates the error backpropagation learning rule for practical applications, and allows pin-to-pin interconnections for multi-chip boards. The developed neuro-board demonstrated active noise canceling without any digital signal processor. Multi-path fading ...

results sixty patients were involved in this study. in asv mode, values including peak airway pressure (p-peak), end-tidal carbon dioxide (etco2), tidal volume and respiratory dead space were significantly lower than simv mode. although the mean value for dynamic compliance had no significant difference in the two types of ventilation, it was better in asv mode. methods in a crossover study, pa...