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 architecture of a neural network with its links and weights can be viewed as a knowledge representation. To overcome the black{box problem of not knowing what knowledge is hidden in a neural architecture, we present a strictly logically operating network. Each neuron represents either a disjunc-tion or conjunction of its inputs and the net thus performs the function of a logic formula. This...

security term in mobile ad hoc networks has several aspects because of the special specification of these networks. in this paper a distributed architecture was proposed in which each node performed intrusion detection based on its own and its neighbors’ data. fuzzy-neural interface was used that is the composition of learning ability of neural network and fuzzy ratiocination of fuzzy system as...

Although Hopfield neural network is one of the most commonly used neural network models for auto-association and optimization tasks, it has several limitations. For example, it is well known that Hopfield neural networks has limited stored patterns, local minimum problems, limited noise ratio, retrieve reverse value of pattern, and shifting and scaling problems. This research will propose multi...

Although Hopfield neural network is one of the most commonly used neural network models for auto-association and optimization tasks, it has several limitations. For example, it is well known that Hopfield neural networks has limited stored patterns, local minimum problems, limited noise ratio, retrieve reverse value of pattern, and shifting and scaling problems. This research will propose multi...

Traditionally, VLSI implementations of spiking neural nets have featured large neuron counts for fixed computations or small exploratory, configurable nets. This paper presents the system architecture of a large configurable neural net system employing a dedicated mapping algorithm for projecting the targeted biology-analog nets and dynamics onto the hardware with its attendant constraints. Key...

In this paper we develop the gamma neural model, a new neural net architecture for processing of temporal patterns. Time varying patterns are normally segmented into a sequence of static patterns that are successively presented to a neural net. In the approach presented here segmentation is avoided. Only current signal values are presented to the neural net, that adapts its own internal memory ...