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As an advanced artificial intelligence technology, error back-propagation (BP) neural network algorithm has been widely applied to electronics, communications, automation and other fields. However, traditional BP neural network algorithm has the disadvantages, such as inclination to stick into local optima, and slow convergence, which exert a great impact on the processing performance, and also limit...
There has been developed many method for the better convergence and generalization ability of neural network. Multilayer Perceptron (MLP) is made multi hidden layered structure for better performance. But in these types of structures still error from any output classes propagates in the backward direction which has a negative impact on the weight updating as well as overall performance because every...
In human brain the neurons are excited in a dynamic way. The response of different neurons varies widely because of the variation of electrical signal in every neuron. Backpropagation(BP) is a training algorithm where the learning of the Neural Network (NN) is done by a constant learning rate (LR). But to replicate the human brain function, the learning rate should be changed as the excitation of...
Cardiomyopathy refers to gradual weakening of the muscular walls of the cardiac chambers. Due to the hypertrophic condition of the muscular walls, damage and stretching of the muscle may lead to arrhythmias, which is detectable using the ECG. In the past, any deviations from a healthy rhythm provide cardiologists with accurate information regarding the heart condition. However, cardiologists are prone...
Local minimum is incorporated problem in neural network (NN) training. To alleviate this problem, a modification of standard backpropagation (BP) algorithm, called BPCL for training NN is proposed. When local minimum arrives in the training, the weights of NN become idle. If the chaotic variation of learning rate (LR) is included during training, the weight update may be accelerated in the local minimum...
This paper proposes a quantum neural network model using conjugate gradient back-propagation algorithm or QCGBP. The QCGBP model is constructed explicitly. Meanwhile, the learning rules for parameters of the network are also presented. According to the experiment result on TEL-8 datasets, QCGBP network presents better performance than conventional BP network in accelerating the convergence rate without...
Least Squares Support Vector Machine (LS-SVM) is a classic algorithm for regression estimation and classification. But unfortunately, for really large problems, LS-SVM can become highly memory and time consuming. In this paper, we present a simplified algorithm for LS-SVM, called ILS-SVM, which effectively reduces the algorithmic complexity. In order to improve the rate of convergence and overcome...
As the iterations are much, and the adjustment speed is slow, the improvements are made to the standard BP neural network algorithm. The momentum term of the weight adjustment rule is improved, make the weight adjustment speed more quicker and the weight adjustment process more smoother. The simulation of a concrete example shows that the iterations of the improved BP neural network algorithm can...
Several object categorization algorithms use kernel methods over multiple cues, as they offer a principled approach to combine multiple cues, and to obtain state-of-the-art performance. A general drawback of these strategies is the high computational cost during training, that prevents their application to large-scale problems. They also do not provide theoretical guarantees on their convergence rate...
We propose a method that rates the suitability of given templates for template-based tracking in real-time. This is important for applications with online template selection, such as SLAM, where it is essential to track a low number of preferably reliable templates. Our approach is based on simple image features specifically designed to identify texture properties which are problematic for tracking...
This paper proposes a mixed optimization algorithm based on RBF neural network (RBF) and Particle Swarm Optimization (PSO), which is applied to the doorplate recognition for a mobile robot. The centers and widths of RBF neural network are determined with self-increasing clustering algorithm, and the improved particle swarm optimization algorithm is used to optimize their distance from the threshold...
Artificial Neural Networks (ANN) is gaining significant importance for pattern recognition applications particularly in the medical field. A hybrid neural network such as Counter Propagation Neural Network (CPN) is highly desirable since it comprises the advantages of supervised and unsupervised training methodologies. Even though it guarantees high accuracy, the network is computationally non-feasible...
A blind adaptive filter with detection of PAM signal is proposed. By studying the structures of different order PAM signals, a new cost function including channel blind equalization and signal detection is defined. The key of this method is that the new constellations of different order PAM signals can be changed to 2-PAM, so we can used the constant statistical information of 2-PAM to equalize the...
The back-propagation (BP) network is widely recognized as a powerful training tool of the multilayer neural networks (MLNNs). Usually it suffers from a slow convergence rate and often results in local minimums, since it applies the steepest descent method to update the network weights. A variety of related algorithms have been introduced to address that problem. Levenberg-Marquardt algorithm is one...
Backpropagation (BP) learning algorithm is the most widely supervised learning technique which is extensively applied in the training of multi-layer feed-forward neural networks. Many modifications that have been proposed to improve the performance of BP have focused on solving the ldquoflat spotrdquo problem to increase the convergence rate. However, their performance is limited due to the error...
Recently, cost-sensitive data mining has been an area of extensive research interests. Intelligent ant colony classification algorithm is introduced in cost-sensitive data mining method in order to obtain satisfied classification results by interaction of ant individuals. The convergence rate of classification is increased by using of metacost's meta-learning theory. Moreover, Boosting theory is investigated...
We consider the multi-sensor tracking systems. In order to solve the sensor registration in multi-sensor tracking system, we propose a new solution based on improved Bayesian regularization algorithm using neural networks in this paper. The nonparametric nature of this approach guarantees that many different kinds of sensor biases can be registered adequately; Levenberg-Marquardt optimum algorithm...
A new learning principle was introduced recently called the Zero-Error Density Maximization (Z-EDM) and was proposed in the framework of MLP backpropagation. In this paper we present the adaptation of this principle to online learning in recurrent neural networks, more precisely, to the Real Time Recurrent Learning (RTRL) approach. We show how to modify the RTRL learning algorithm in order to make...
This paper presents an idea for enhancing the convergence rate of the blind constant modulus algorithm which is used in spatial filtering (beam forming) of the users in a typical CDMA environment. The convergence rate is increased by initializing the weights of the CMA using a non-blind algorithm least mean squares (LMS) utilizing only a few samples of training sequence. Performance as well as convergence...
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