Chaotic Genetic BP Neural Network Image Segmentation Method Based on Arnold Transform
A BP neural network, image segmentation technology, applied in the field of image processing, can solve the problems of low initial population ergodicity, complex individual operations, slow convergence speed, etc.
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Embodiment 1
[0055] The method of this embodiment comprises the following steps: 1) set up BP neural network; 2) obtain the initial weight value and initial threshold value of BP neural network according to the optimal solution obtained by chaotic genetic algorithm; 3) bring initial threshold value and initial weight value into In the BP neural network, the input data is used for training, and the weights and thresholds of the BP neural network are updated by the error obtained each time, and the trained BP neural network is obtained through repeated iterations; 4) image segmentation is performed using the trained BP neural network ;
[0056] The specific process of obtaining the initial weights and initial thresholds of the BP neural network based on the optimal solution obtained by the chaotic genetic algorithm is as follows:
[0057] ①Initialize population: generate population p by chaotic mapping method, divide population p into initial population x and population y to be optimized; th...
Embodiment 2
[0135] It is found in the research that the above-mentioned chaotic perturbation in the mutation process can improve the local search ability of the algorithm, but it has the following shortcomings: chaotic perturbation completely replaces the random mutation of the population and ignores the influence of the mutation rate in different evolutionary periods on the search results. Individual chaotic perturbation reduces the search efficiency, so this embodiment improves the chaotic mutation strategy:
[0136] When performing the chaotic mutation operation, the adaptive mutation is performed first, and then the fitness value of the adaptive mutation individual is calculated, the fitness value is sorted according to the high and low, and the previously preset number of individuals with higher fitness values are selected as excellent individuals. Perform chaotic mutation operations on the remaining individuals according to Step1-Step4. Wherein, the preset number is preferably 10%...
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