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Hardware circuit of recursive neural network of LS-SVM classification and returning study and implementing method
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A recurrent neural network, LS-SVM technology, applied in the field of pattern recognition
Inactive Publication Date: 2010-06-02
XIAN UNIV OF TECH
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[0006] Another object of the present invention is to provide a method for implementing LS-SVM classification and regression learning recursive neural network hardware circuits, so that the problems of classification and regression learning can be solved by simulating hardware circuits
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example 1
[0113] z 1 =(1.5,2),z 2 =(2,1),z 3 =(1.5,1),z 4 =(3,4),z 5 =(4, 3), and its categories are (+1, +1, -1, -1, -1) respectively.
[0114] Step 1: Construct the topology structure of the SVM classification learning recurrent neural network according to the number of 5 samples;
[0115] Step 2: Use Gaussian kernel function, choose σ=1.5, γ -1 = 0.20, and calculate
[0116] Step 3: select corresponding module according to classification neurorecurrent network topology and carry out Simulink simulation based on Matlabsoftware;
[0117] Step 4: Select R 0 =1kΩ, C=1μF, calculate the resistance R of each weight 0 / |q ij |, and use the "rounding" method to select the nominal resistance value as close as possible;
[0118] Step 5: According to figure 2 The PCB hardware circuit is made according to the structure, in which the package of the resistor is AXIAL0.4, the package of the operational amplifier is DIP8, the package of the capacitor is RB.2 / .4, and the integral part i...
example 2
[0129] Table 2 Function values of 9 points
[0130]
[0131] Step 1: Construct the topology structure of the SVM regression neural network according to the number of 9 samples;
[0132] Step 2: Use Gaussian kernel function, where σ=1, select γ -1 =0.01, and calculate Ω according to the sample points ij =K(x i , x j )=φ(x i ) T φ(x j );
[0133] Step 3: Select the corresponding module according to the topology of the regression neural network to carry out Simulink simulation based on Matlabsoftware;
[0134] Step 4: Select R 0 =1kΩ, C=1μF, calculate the resistance R of each weight 0 / |Ω ij |, and use the "rounding" method to select the nominal resistance value as close as possible;
[0135] Step 5: Due to the resistance value R 0 / |Ω ij |It is very large in theory, close to the MΩ level, so the correctness of the analog circuit is verified in the form of Pspice simulation. The operational amplifier used in the analog implementation circuit is μA741, and all ...
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Abstract
The invention discloses an LS-SVM classification and regression study recursive neural network hardware circuit and a realization method; the method combines the LS-SVM method with the recursive neural network to deduce a dynamic equation and a topological structure describing the neural network, and further establishes a hardware circuit for realizing the recursive neural network, so that the hardware circuit is used to realize the least square support vector machinealgorithm. Compared with the existing network, the LS-SVM classification and regression study recursive neural network described by the invention eliminates the non-linear part of the network, so the neural network structure is simplified and the SVM training speed is greatly improved; meanwhile, the LS-SVM study neural network provided by the invention can realize classification and regression, on the basis of nearly unchanging the topological structure.
Description
technical field [0001] The invention belongs to the technical field of pattern recognition, and relates to a LS-SVM classification and regression learning recursive neural network hardware circuit, and also relates to a realization method of the hardware circuit. Background technique [0002] Support vector machines (Support Vector Machines, SVMs) adopt the idea and method of structural risk minimization, with the advantages of good generalization ability, extremely low classification and approximation error, mathematical tractability and concise geometric explanation, etc. Widely used as a classification and regression tool. At present, the research on SVM mainly focuses on theoretical research and algorithm optimization. In contrast, there are relatively few researches on its application research and algorithm implementation, and there are only limited experimental research reports at present. At the same time, most of these algorithms can only be realized by computer so...
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