in this paper, the application of neural networks for simulation and optimization of the cogeneration systems has been presented. cgam problem, a benchmark in cogeneration systems, is chosen as a casestudy. thermodynamic model includes precise modeling of the whole plant. for simulation of the steadysate behavior, the static neural network is applied. then using dynamic neural network, plant is...

the proposed iafc neural networks have both stability and plasticity because theyuse a control structure similar to that of the art-1(adaptive resonance theory) neural network.the unsupervised iafc neural network is the unsupervised neural network which uses the fuzzyleaky learning rule. this fuzzy leaky learning rule controls the updating amounts by fuzzymembership values. the supervised iafc ...

This paper presents an approach to the use of neural networks to improve iterative learning control performance. The neural networks are used to estimate learning gain of an iterative learning law and to store the learned control input prooles for diierent reference trajectories. A neural network of piecewise linear approximation is presented to eeectively identify the system dynamics, and the ...

allocation and removing of excess water from the irrigation and drainage network is one of the most important activities in the management of these networks. side weir is one of the most common structures for this purpose. study on the flow hydraulic characteristics of this structure included two parts, defining the water surface profiles and estimating the discharge coefficient. to estimate th...

In this paper the performance of Artificial Neural Networks (ANNs) and Adaptive Neuro- Fuzzy Inference Systems (ANFIS) in simulating the inverse dynamic behavior of Magneto- Rheological (MR) dampers is investigated. MR dampers are one of the most applicable methods in semi active control of seismic response of structures. Various mathematical models are introduced to simulate the dynamic behavi...

It is difficult to realize adaptive control for some complex nonlinear processes which are operated in different environments and when operation conditions are changed frequently. In this paper we propose an identifier-based adaptive control (or indirect adaptive control). The identifier uses two effective tools: multiple models and neural networks. A hysteresis switching algorithm is applied t...

The paper presents a new hybridization methodology involving Neural, Fuzzy and Rough Computing. A Rough Sets based approximation technique has been proposed based on a certain Neuro – Fuzzy architecture. A New Rough Neuron composition consisting of a combination of a Lower Bound neuron and a Boundary neuron has also been described. The conventional convergence of error in back propagation has b...

a local scouring phenomenon is one of the important problems in hydraulic design of groynes. due to constriction and downward flow, the scouring can occur around the groynes. nowadays, the artificial neural networks have a lot of applications in various water engineering problems where there is not any specific relation between effective parameters. in this study, the artificial neural networks...

Natural deduction is essentially a sequential decision task, similar to many game-playing tasks. Such a task is well suited to benefit from the techniques of neuro-evolution. Symbiotic, Adaptive Neuro-Evolution (SANE)(Moriarty and Miikkulainen 1996) has proven successful at evolving networks for such tasks. This paper will show that SANE can be used to evolve a natural deduction system on a neu...