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This paper presents an application of cognitive networking paradigm to the problem of inter-cell interference coordination (ICIC) in Long-Term Evolution-Uplink (LTE-UL). We describe state-of-the-art, research challenges involved, and a novel random neural network (RNN) based power controller and interference management framework. The RNN based cognitive engine (CE) learns how the electromagnetic environment...
Learning rate and momentum coefficient are critical parameters on back propagation algorithm because of their effect on learning speed and deviation ratio from global minimum. Hidden neuron number has an effect on classification accuracy, and excessive number of hidden neuron causes to increase the operation load. Because these parameters are selected randomly, finding the accurate values requires...
This paper proposes the application of a new binary particle swarm optimization (BPSO) method to feature selection problems. Two enhanced versions of binary particle swarm optimization, designed to cope with premature convergence of the BPSO algorithm, are proposed. These methods control the swarm variability using the velocity and the similarity between best swarm solutions. The proposed PSO methods...
Automatic recognition of the communication signals (ARCS)plays an important role for various applications. this paper presents a hybrid intelligent system that automatically recognizes a variety of digital communication signals. The hybrid system includes three main modules: feature extraction module, classifier module and optimization module. In the feature extraction module, we have used the balanced...
The paper deals with the design and development of classifiers and, in particular, with the problem of selecting the most relevant input variables to be used as inputs for classification purpose in practical applications. In many real problems the selection of input variables is a very important task: often real datasets used for developing a classifier contain a high number of inputs but no a priori...
An algorithm for evolving recurrent neural network via the genetic algorithm was implemented on the CUDA, resulting in a system called CuParcone (CUDA based Partially Connected Neural Evolutionary). Run on a Nvidia Tesla “GPU supercomputer, ” CuParcone achieves a performance increase of 323 times in face gender recognition compared to the comparable Parcone algorithm on a state-of-the-art, commodity...
The problem of forecast belongs to an input-output nonlinear system in nature. And most of problems which need to be forecasted have a large number of predictors which are relatively correlated. Therefore neural network has unique superiority in dealing with such problems. But when traditional BP (Back-Propagation Network) neural network is used to predict, there are many inadequacies in predictive...
A technique to optimize the Standard Additive Model (SAM) fuzzy system for nonlinear system approximation is presented. First, fuzzy rules are initialized more much than usual by employing Centroid Neural Network (CNN) and then the genetic algorithm-based optimization process used to exclude unnecessary and redundant rules; thereafter, the fuzzy rule parameters are tuned by the gradient descent method...
Transit vehicle reasonable dispatching is very important to solve the congestion of traffic. Artificial neural network is the common dispatching method, among which RBF neural network is a feed-forward neural network with one hidden layer, which can uniformly approximate any continuous function to a prospected accuracy. In RBF neural network, the choice of the widths and centers of the Gaussian function,...
Application of online system identification based on improved quantum-behaved particle swarm optimization is studied in this paper. QPSO algorithm combined with the single neuron can improve the local search capabilities and identification accuracy. Then the improved QPSO is applied to online identify parameters of a system described by differential equations and compared with the improved particle...
A fast version of probabilistic neural network model is proposed. The model incorporates the J-means algorithm to select the pattern layer centers and genetic algorithm to optimize the spread parameters of the probabilistic neural network, enhancing its performance. The proposed approach is applied to the recognition of degraded traffic signs with promising results. In order to cope with the degradations,...
Considering the issues that the relationship between the fault of oil pump existent and fault information is a complicated and nonlinear system, and it is very difficult to found the process model to describe it. According to the physical circumstances of oil pump, a fault diagnosis method of oil pump based on high speed and precise genetic algorithm neural network is presented in this paper. The...
Because of the complicated interaction of the sludge compost components, it makes the compost quality evaluation system appear the non-linearity and uncertainty. According to the physical circumstances of sludge compost, a compost quality evaluation modeling method based on high speed and precise genetic algorithm neural network is presented. The high speed and precise genetic algorithm neural network...
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