In this paper, performance oriented control laws are synthesized for a class of single-input-single-output (SISO) n-th order nonlinear systems in a normal form by integrating the neural networks (NNs) techniques and the adaptive robust control (ARC) design philosophy. All unknown but repeatable nonlinear functions in the system are approximated by the outputs of NNs to achieve a better model co...

to Optimize Neural Network Stru tures Kunikazu Kobayashiy and Masanao Ohbayashi Department of Computer S ien e & Systems Engineering, Fa ulty of Engineering, Yamagu hi University ye-mail: k sse.yamagu hi-u.a .jp Abstra t A new en oding method for optimizing neural network stru tures is proposed. It is based on an indire t en oding method with variable length gene ode. The sear h ability for ndi...

We describe in this paper the evolution of modular neural networks using hierarchical genetic algorithms for pattern recognition. Modular Neural Networks (MNN) have shown significant learning improvement over single Neural Networks (NN). For this reason, the use of MNN for pattern recognition is well justified. However, network topology design of MNN is at least an order of magnitude more diffi...

abstract: this paper represents a novel use of artificial neural networks in medical science. the proposed technique involves training a multi layer perceptron (mlp) (a kind of artificial neural network) with a bp learning algorithm to recognize a pattern for the diagnosing and prediction of five blood disorders, through the results of blood tests from h1 machine. the blood test parameters and ...

The paper discusses the location-allocation model for logistic networks and distribution centers through considering uncertain parameters. In real-world cases, demands and transshipment costs change over the period of the time. This may lead to large cost deviation in total cost. Scenario based robust optimization approaches are proposed where occurrence probability of each scenario is not know...

This paper proposed a novel model for short term load forecast (STLF) in the electricity market. The prior electricity demand data are treated as time series. The model is composed of several neural networks whose data are processed using a wavelet technique. The model is created in the form of a simulation program written with MATLAB. The load data are treated as time series data. They are dec...

  Managing products’ end-of-life and recovery of used products is gaining significant importance during last years. Therefore, managing the reverse flow of products can be an important potential for winning consumers in future competitive markets. In this context, establishing reverse logistics networks is becoming a main problem in reverse supply chains. Genetic Algorithm (GA) is utilized to s...