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The work presented in this paper concerns the detection of drowsy driving based on time series measurements of driving behavior. Artificial neural networks, trained using particle swarm optimization, have been used to combine several indicators of drowsy driving based on a data set originating from a large study carried out in the driving simulator at the Swedish National Road and Transportation Institute...
This paper describes a new technique for training feedforward neural networks. We employ the proposed algorithm for robust neural network training purpose. Conventional neural network training algorithms based on the gradient descent often encounter local minima problems. Recently, some evolutionary algorithms are getting a lot more attention about global search ability but are less-accurate for complicated...
Routing is very important for computer networks because it is one of the main factors that influences network performance. In this paper, we propose an improved intelligent method for routing based on Hopfield Neural Networks (HANN), which uses a discrete equation and the Particle Swarm Optimization (PSO) technique to optimize the HNN parameters. The fitness function for the PSO algorithm used here...
Based on the perfect properties of stochastic particle swarm optimization (SPSO), such as the property of robust and quick convergence, a new scheme is applied to estimate scaling factor for radar constant false alarm rate (CFAR) detectors. Owing to few constraints, it can estimate scaling factor for single radar as well as radar netting system. The numerical results indicate that the particle swarm...
This paper introduces a neurochaotic information processor based upon perturbed Duffing equation. The proposed chaotic neural network has parameters to tune by which decision is made to behave either chaotically or periodically. The neurochaotic nonlinear network adopts the chaotic dynamics of so-called Duffing oscillator for the chaotic movement in the search space. It then uses the benefits of fast...
An hybrid particle swarm optimization PSO-based wavelet neural network for modelling the development of fluid dispensing for electronic packaging is presented in this paper. In modelling the fluid dispensing process, it is important to understand the process behaviour as well as determine optimum operating conditions of the process for a high-yield, low cost and robust operation. Modelling the fluid...
Selecting relevant genes from microarray data poses a huge challenge due to the high-dimensionality of the features, multi-class categories and a relatively small sample size. The main task of the classification process is to decrease the microarray data dimensionality. In order to analyze microarray data, an optimal subset of features (genes) which adequately represents the original set of features...
Adaboost is an ensemble learning algorithm that combines many base-classifiers to improve their performance. Starting with Viola and Jonespsila researches, Adaboost has often been used to local feature selection for object detection. Adaboost by Viola-Jones consists of following two optimization schemes: (1) training of the local features to make base-classifiers, and (2) selection of the best local...
A new hybrid algorithm based on particle swarm optimization (PSO), evolutionary algorithm (EA), and differential evolution (DE) is presented for training a recurrent neural network (RNN) for multiple-input multiple-output (MIMO) channel prediction. The hybrid algorithm is shown to be superior in performance to PSO and differential evolution PSO (DEPSO) for different channel environments. The received...
This paper presents a new face recognition approach by using correlation analysis and ensemble classifiers based on support vector machine (SVM). In this approach, image pre-processing techniques such as histogram equalization, edge detection and geometrical transformation are first used in order to improve the quality of the face images. We further employ correlation analysis method to extract features...
Turbo Codes present a new direction for the channel encoding, especially since they were adopted for multiple norms of telecommunications, such as deeper communication, etc. To obtain an excellent performance, it is necessary to design robust turbo code interleaver and decoding algorithms. In this paper, we are investigating particle swarm algorithm as a promising optimization method to find good...
We describe in this paper a new hybrid approach for optimization combining particle swarm optimization (PSO) and genetic algorithms (GAs) using fuzzy logic to integrate the results. The new evolutionary method combines the advantages of PSO and GA to give us an improved PSO+GA hybrid method fuzzy logic is used to combine the results of the PSO and GA in the best way possible. The new hybrid PSO+GA...
In this paper, statistical experimental design is used to characterize the contact formation process for high-performance silicon solar cells. Central composite design is employed, and neural networks trained by the error back-propagation algorithm are used to model the relationships between several input factors and solar cell efficiency. Subsequently, both genetic algorithms and particle swarm optimization...
Many methods for solving optimization problems, whether direct or indirect, rely upon gradient information and therefore may converge to a local optimum. Global optimization methods like evolutionary algorithms, overcome this problem. In this work it is investigated how to construct a quality BBF network for a specific application can be a time-consuming process as the system must select both a suitable...
The multi-robot task allocation (MRTA) especially in unknown complex environment is one of the fundamental problems, a mostly important object in research of multi-robot. The MRTA problem is initially formulated as a chance-constrained optimization problem. Monte Carlo simulation is used to verify the accuracy of the solution provided by the algorithm. Ant colony optimization (ACO) algorithm based...
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