A Spectral Clustering Algorithm Improved by P Systems

Using spectral clustering algorithm is diffcult to find the clusters in the cases that dataset has a large difference in density and its clustering effect depends on the selection of initial centers. To overcome the shortcomings, we propose a novel spectral clustering algorithm based on membrane computing framework, called MSC algorithm, whose idea is to use membrane clustering algorithm to realize the clustering component in spectral clustering. A tissue-like P system is used as its computing framework, where each object in cells denotes a set of cluster centers and velocity-location model is used as the evolution rules. Under the control of evolutioncommunication mechanism, the tissue-like P system can obtain a good clustering partition for each dataset. The proposed spectral clustering algorithm is evaluated on three artiffcial datasets and ten UCI datasets, and it is further compared with classical spectral clustering algorithms. The comparison results demonstrate the advantage of the proposed spectral clustering algorithm

A New Method for Chinese Character Strokes Recognition

ABSTRACT In this paper, the problem of stroke recognition has been studied, and the strategies and the algorithms related to the problem are proposed or developed. Based on studying some current methods for Chinese characters strokes recognition, a new method called combining trial is presented. The analysis and results of experiments showed that the method has the advantage of high degree of steadiness

Interval-valued fuzzy spiking neural P systems for fault diagnosis of power transmission networks

It is a challenge problem how to deal with the uncertainty in fault diagnosis of power systems. To solve the challenge problem, this paper introduces an interval-valued fuzzy spiking neural P system (IVFSNP system), where the interval-valued fuzzy logic is integrated into spiking neural P systems to characterize the uncertainty. Based on the IVFSNP system, a fuzzy reasoning algorithm is presented, and the corresponding fault diagnosis model is developed. IVFSNP system is capable of describing the incomplete and uncertain fault signals from a supervisory control and data acquisition system equipped together with electric power systems. In order to evaluate the availability and effectiveness of the proposed fault diagnosis model, two case studies of fault diagnosis of a transmission network are discussed and analyzed, including complex and multiple fault situations with the incomplete and uncertain status signals. The results of the case studies demonstrate that IVFSNP system can be used to diagnose the faulty sections in power transmission networks accurately and effectively.Research Fund of Sichuan Science and Technology Project 2018JY0083Chunhui Project Foundation of the Education Department of China Z2016143Chunhui Project Foundation of the Education Department of China Z2016148Research Foundation of the Education Department of Sichuan province 17TD003