Research On Memcomputing Model And Its Application On Image Edge Detection

The memristive elements include memristor,memristor container and memristor,which have the characteristics of storage and calculation.With memristive elements as the core,the memcomputing model has the properties that traditional models do not have and can be applied in many fields.However,there are few literatures about the application of memory computing model to image edge detection.In this context,this thesis studies the memcomputing model based on memristive elements.Two new memcomputing models,memristive filter model and memristive genetic algorithm model are designed.The feasibility of the algorithm is proved by the realization of image edge detection algorithm.The specific research content is as follows.1.Research on modeling of memristive filterThe memristor and the memcapacitor are introduced into the filter circuit at the same time.The designed filter circuit is called memristive filter,including memristive low-pass filter,memristive high-pass filter,memristive band-pass filter and memristive band-stop filter.The feasibility of the method is proved by experimental simulation.Compared with the other filters(without memristive elements),the memristive filter has the important characteristic of time-varying,which enables the memristive filter to meet different application environments under the same implementation.2.Research on modeling of memristive genetic algorithmThe memristive network is used to map the genetic algorithm,and the memristive genetic algorithm model is realized.The memristor with storage and computing characteristics provides similarity correlation between electronic devices and genetic genes and can effectively implement the genetic algorithm model.Meanwhile,the variability of memristive network structure can correspond to various genetic operations of genetic algorithm model.3.Simulation implementation of new image edge detection algorithmA new image edge detection algorithm is designed based on the two memcomputing models.The algorithm uses a memristive filter for filtering preprocessing to suppress the noise in the image and enhance the image,and then uses the memristive genetic algorithm to realize the image edge detection.Experimental results show that the algorithm is feasible and effective.Then the edge detection algorithm is analyzed and the influence of different parameters on the performance of the algorithm is discussed.