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In recent years, facial expressions of pain have been the focus of considerable behavioral research. Such work has documented that pain expressions, like other affective facial expressions, play an important role in social communication. Enabling computer systems to recognize pain expression from facial images is a challenging research topic. In this paper, we present two systems for pain recognition...
With the development of market economy in China, the problem of bad debt becomes increasingly serious in enterprises. In this paper, a bad-debt-risk evaluation model is established based on LS-SVM classifier, using a new set of index system which combines financial factors with non-financial factors on the basis of the 5C system evaluation method. The bad debt rating is separated into four classes-...
Measurement of visual quality is of fundamental importance to some image processing applications. And the perceived image distortion of any image strongly depends on the local features, such as edges, flats and textures. Since edges often convey much information of an image, we propose a novel algorithm for image quality assessment based on the edge and contrast similarity between the distorted image...
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 have been proposed to improve the performance of BP, and BP with Magnified Gradient Function (MGFPROP) is one of the fast learning algorithms which improve both the convergence rate and the global convergence...
A novel approach for data mining of steam turbine based on neural network and genetic algorithm is brought forward, aimed at overcoming shortages of some current knowledge attaining methods. The historical fault data of steam turbine is processed with fuzzy and discrete method firstly, a multiplayer backpropagation neural network is structured secondly, the neural network is trained via teacherpsilas...
This study used smooth support vector regression and back propagation network as the basic theory in study of the mutual fund performance prediction. This paper used return on performance and return on market to make a comparison, and through the risk values, explored each modelpsilas advantages and disadvantages. This study used Taiwanpsilas equity fund as the prediction target, the validation study...
In a fully complex-valued feed-forward network, the convergence of the complex-valued back-propagation learning algorithm depends on the choice of the activation function, minimization criterion, initial weights and the learning rate. The minimization criteria used in the existing learning algorithms do not approximate the phase well in complex-valued function approximation problems. This aspect is...
Traditional neural network approaches for traffic flow forecasting are usually single task learning (STL) models, which do not take advantage of the information provided by related tasks. In contrast to STL, multitask learning (MTL) has the potential to improve generalization by transferring information in training signals of extra tasks. In this paper, MTL based neural networks are used for traffic...
Much recent research activity has focused on the theory and application of quantum calculus. This branch of mathematics continues to find new and useful applications and there is much promise left for investigation into this field. We present a formulation of dynamic programming grounded in the quantum calculus. Our results include the standard dynamic programming induction algorithm which can be...
Based on nonlinear mapping relationship between fault symptom and fault type in subsystems of FOG SINS (fiber-optic gyroscope strapdown inertial system), BP (back propagation) and Elman neural network approaches were presented for fault diagnosis. Fault mechanism and failure behavior of FOG SINS was analyzed, then featured fault types were extracted from FOG SINS faults and the extracted features...
A constrained-backpropagation training technique is presented to suppress interference and preserve prior knowledge in sigmoidal neural networks, while new information is learned incrementally. The technique is based on constrained optimization, and minimizes an error function subject to a set of equality constraints derived via an algebraic training approach. As a result, sigmoidal neural networks...
A modular neural network works by dividing the input domain into segments, assigning a separate neural network to each sub-domain. This paper introduces the self-splitting modular neural network, in which the partitioning of the input domain occurs during training. It works by first attempting to solve a problem with a single network. If that fails, it finds the largest chunk of the input domain that...
This paper investigates the problem of feature subset selection as part of a methodology that integrates heuristic tabu search, simulated annealing, genetic algorithms and backpropagation. This technique combines both global and local search strategies for the simultaneous optimization of the number of connections and connection values of multi-layer perceptron neural networks. We compare the performance...
Multilayer perceptron is adopted for well log data inversion. The input of the neural network is the apparent resistivity (Ra) of the well log and the desired output is the true formation resistivity (Rt). The higher order of the input features and the original features are the network input for training. Gradient descent method is used in the back propagation learning rule. From our experimental...
This paper uses a combination of K-Iterations Fast Learning Artificial Neural Network (KFLANN) and Gabor filters to create a Gabor signature classifier. Gabor filters are known to be useful in modeling responses of the receptive fields and the properties of simple cells in the visual cortex. The responses produced by Gabor filters produce good quantifiers of the visual content in any given image....
Artificial neural networks (ANNs) are promising approaches for financial time-series prediction. This study adopts a hybrid approach, called a fuzzy BPN, consisting of a back-propagation neural network (BPN) and a fuzzy membership function which takes advantage of the ANNspsila nonlinear features and interval values instead of the shortcoming of ANNspsila single-point estimation. To employ the two...
Feature extraction is a key element of pattern recognition for myoelectric control. In this paper, recurrence plots and recurrence quantification analysis (RQA) are used as the feature extractor for surface EMG signals. For eight different hand motions, two-channel EMG signals are recorded. Ten individual RQA parameters are calculated for each channel of EMG signals. With different combinations of...
This research employs an artificial neural network with a variable mathematic structure that is capable of simulating a nonlinear structural system. A back-propagation neural network (BPN) is adopted to estimate outflow for an ungauged area by considering temporal distribution of rainfall-runoff and the spatial distribution of watershed environment. The nonlinear relationship among the physiographic...
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...
This article presents the implementation of position control of a mobile inverted pendulum(MIP) system by using the radial basis function network(RBF). The MIP has two wheels to move on the plane and to balance the pendulum. The MIP is known as a nonlinear system whose dynamics is non-holonomic. The goal is to control the MIP to maintain the balance of the pendulum while tracking a desired position...
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