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This paper presents a new advance in Neuro-space mapping (Neuro-SM) techniques for modeling nonlinear microwave devices. Suppose that existing device models (namely, coarse models) cannot match the behavior of a new device (referred to as the fine model). By neural network mapping of the voltage and current signals from the coarse to the fine models, Neuro-SM can modify the behavior of the coarse...
This paper applies the principle of the immune system adjustment to optimize the structure parameters of wavelet network, so as to establish a new type of wavelet neural network model which will be applied to turbine exhaust steam enthalpies. The calculation results show that the model has fast convergence, simple operation, high accuracy in forecasting, and has certain value of engineering applications.
A novel application to the optimization of neural networks is presented in this paper. Here, the weight and architecture optimization of neural networks can be formulated as a mixed-integer optimization problem. And then a mixed-integer evolutionary algorithm (Mixed-Integer Hybrid Differential Evolution, MIHDE) is used to optimize the neural network. Finally, the optimized neural network is applied...
Because traditional approaches for solving the simultaneous localization and map building (SLAM) problem have the limitation of computational complexity, imprecision of filter algorithm and fragile data association, soft computing technique has been used to solve the problem. In this paper, we reviewed the state of the art of the application of evolutionary algorithm, fuzzy logic and neural networks...
Computer security is very important in these days. Computers are used probably in any industry and their protection against attacks is very important task. The protection usually consist in several levels. The first level is preventions. Intrusion detection system (IDS) may be used as next level. IDS is useful in detection of intrusions, but also in monitoring of security issues and the traffic. This...
Particle swarm optimization (PSO) was introduced by Kennedy and Eberhart in 1995 as a population based stochastic search and optimization process. The natural behavior of a bird flock when searching for food is simulated through the movements of the individuals (particles or living organisms) in the flock. The goal is to converge to the global optimum of some multidimensional function. PSO is conceptually...
The design of printed reflectarray antennas (RAs) could be quite complex and computationally expensive, since the need of providing high performances and satisfying requirements that could be also in contrast each other could require the use of a large number of re-radiating advanced element configurations. A possible strategy for the RA design could be therefore of carrying it out adopting an evolutionary...
A evolutionary programming is proposed in this paper to automatically design neural networks(NNS) ensembles. Based on negative correlation learning, different individual NNs in the ensemble can learn to subdivide the task and thereby solve it more efficiently and elegantly. At the same time, different individual NNs are always to find the best collaboration connection during the evolutionary process...
POLSAR image classification plays an important role in remote sensing. POLSAR data are a type of mass data and have more independent features which can represent different physical significances than optical image. Therefore, POLSAR image classification is actually a high dimensional nonlinear mapping problem. Because of the nonlinear mapping function of BP neural network, it can be used to classify...
Any neuro-evolutionary algorithm that solves complex problems needs to deal with the issue of computational complexity. We show how a neural network (feed-forward, recurrent or RBF) can be transformed and then compiled in order to achieve fast execution speeds without requiring dedicated hardware like FPGAs. The compiled network uses a simple external data structure-a vector-for its parameters. This...
Imperialist Competitive Algorithm (ICA) is a novel optimization algorithm that inspired by socio-political process of imperialistic competition. ICA shown its excellent capability in diverse optimization tasks. In this paper, a new method for training an Artificial Neural Network using Chaotic Imperialist Competitive Algorithm is proposed. In Chaotic Imperialist Competitive Algorithm (CICA) the chaos...
Solving multi-objective optimization problems (MOPs) using evolutionary algorithms (EAs) has been gaining a lot of interest recently. Go is a hard and complex board game. Using EAs, a computer may learn to play the game of Go by playing the games repeatedly and gaining the experience from these repeated plays. In this project, artificial neural networks (ANNs) are evolved with the Pareto Archived...
A novel application to the optimization of artificial neural networks (ANNs) is presented in this paper. Here, the weight and architecture optimization of ANNs can be formulated as a mixed-integer optimization problem. And then a mixed-integer evolutionary algorithm (Mixed-Integer Hybrid Differential Evolution, MIHDE) is used to optimize the ANN. Finally, the optimized ANN is applied to the prediction...
Heuristic constructive algorithms have been widely and successfully applied to the solution of routing problems. Since they generally consist of an iterative insertion of nodes to construction routes, prioritization rules for assignments is critic for algorithm's performance. Developing these rules is time consuming and relies much on researcher skills and knowledge on problem features. This paper...
BP network training algorithm is based on the error gradient descent to modify weights, which leads to the inevitable problem of a local minimum point. Some researchers have presented some amending ways and made some remarkable achievements. But combining others algorithm for adjusting the weights of BP network is few. At present, a new evolution algorithm called as differential evolution is used...
In order to model uncertain optimization problems, fuzzy expected value programming and chance-constrained programming are formulated. Firstly, the method of fuzzy simulation is used to generate training samples for neural network, and the neural network is embedded into PSO to design a hybrid intelligent algorithm. Then, the death penalty function is adopted to deal with the constraints. Finally,...
The increasing interest among the scientific community on soft computing and optimization techniques in recent years leads to develop effective ad-hoc procedures for electromagnetic structures modelling, often based on evolutionary iterative algorithms. In fact, in most of engineering problems, numerical simulations could be very computationally expensive. An example of this is for instance the design...
Product unit neural network (PUNN) training is formulated as an optimization problem and then particle swarm optimization (PSO), an emerging evolutionary computation algorithm, is employed to resolve it. A simple and effective encoding scheme for particles is proposed by which PSO algorithm can configure the architecture and weight of PUNN simultaneously depending on training sets. Because the training...
A hybrid algorithm based on Extremal Optimization (EO) with adaptive levy mutation and Differential Evolution (HEODE) was proposed in this paper. It applied the idea of combination mechanism of global and local search. In the process of the global search, DE is an evolutionary algorithm based on the difference in group that can quickly approach a approximate optimal solution. During the local search,...
Multi-processor Systems-on-chip are currently designed by using platform-based synthesis techniques. In this approach, a wide range of platform parameters are tuned to find the best trade-offs in terms of the selected system figures of merit (such as energy, delay and area). This optimization phase is called Design Space Exploration (DSE) and it generally consists of a Multi-Objective Optimization...
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