Showing papers by "Jun Wang published in 2017"

Multiobjective fuzzy clustering approach based on tissue-like membrane systems

Abstract: We develop a multiobjective clustering framework to deal with fuzzy clustering problem.We design a tissue-like membrane system with a special cell structure, including evolution cells and memory cells.We develop a modified differential evolution rule for object evolution. Fuzzy clustering problem is usually posed as an optimization problem. However, the existing research has shown that clustering technique that optimizes a single cluster validity index may not provide satisfactory results on different kinds of data sets. This paper proposes a multiobjective clustering framework for fuzzy clustering, in which a tissue-like membrane system with a special cell structure is designed to integrate a non-dominated sorting technique and a modified differential evolution mechanism. Based on the multiobjective clustering framework, a fuzzy clustering approach is realized to optimize three cluster validity indices that can capture different characteristics. The proposed approach is evaluated on six artificial and ten real-life data sets and is compared with several multiobjective and singleobjective techniques. The comparison results demonstrate the effectiveness and advantage of the proposed approach on clustering the data sets with different characteristics.

Competitive Spiking Neural P Systems With Rules on Synapses

Abstract: This paper proposes an extension of spiking neural P systems with rules on synapses (SNP-RS systems) working in competitive strategy, called competitive SNP-RS (CSNP-RS systems). In CSNP-RS systems, the spikes are viewed as a kind of competitive resources, and the rules on different synapses will compete the spikes (resources) in neurons. A new strategy is considered: the total amount of spikes consumed by these rules should be greater than or equal to the generated amount of spikes. There are two cases to chosen one rule non-deterministically: 1) two or more rules on the same synapse are enabled; and 2) two or more rules on the different synapses are enabled, and the number of spikes contained in neuron is smaller than the number of spikes consumed by these rules. CSNP-RS systems are a kind of distributed parallel computing models. The computational power of CSNP-RS systems is investigated. Specifically, we prove that CSNP-RS systems are turing universal as number generating/accepting devices and function computing device.

Fault diagnosis for multi-energy flows of energy internet: Framework and prospects

Abstract: Energy Internet (EI) is an inevitable development trend of energy systems under the background of technology development, environmental pressure and energy transition. Multi-energy flow coupling is one of the key characteristics of the EI, which enhances the interoperability of different types of energy flows while consequently increases the probability of cascading failures. Therefore it is of great significance to study the multi-energy flow fault diagnosis of the EI to ensure its safe and stable operation as well as the continuous energy supply. This paper introduces the concept of multi-energy flow cascading fault of the EI for the first time. The energy internet framework for multi-energy flow cascading fault diagnosis is firstly proposed, and then characteristics of various energy networks in the EI are analyzed from the perspective of fault diagnosis. Finally, future research prospects are discussed.

A kNN classifier optimized by P systems

Abstract: We propose a k-nearest neighbors (kNN) classification algorithm optimized by P systems in this article, called kNN-P, which can improve the performance of the original kNN classifier. A P system consisting of several cells is considered as its computational framework. Under the control of both evolution rules and communication rules, each cell determines the optimal set of k-nearest neighbors for a sample. The proposed kNN-P is evaluated on 18 benchmark datasets and compared with classical kNN algorithm and 8 recently developed improved algorithms. Comparison results demonstrate the availability and effectiveness of the proposed algorithm.

Distributed Fuzzy P Systems with Promoters and Their Application in Power Balance of Multi-microgrids

Abstract: This paper proposes distributed fuzzy P systems with promoters for multi-microgrids power balance, where the distributed P systems (dP systems) differ from other P systems with the ability to handle distributed input problems, which makes themselves more suitable for solving control problems. To make full use of the advantages of dP systems and provide a research idea for the power balance of multi-microgrids, promoters and fuzzy theory are introduced into dP systems to characterize a large amount of uncertain and inaccurate information. Moreover, the proposed distributed fuzzy P systems with promoters are applied to fulfill the power balance of multi-microgrids. Finally, the power balance in multi-microgrids as well as the balance between the multi-microgrids and their connecting grid is realized by three cases.

An Improved Spiking Neural P Systems with Anti-Spikes for Fault Location of Distribution Networks with Distributed Generation

Abstract: This paper proposes a method for fault location in distribution networks with distributed generation based on an improved spiking neural P system with anti-spikes (IASNP system). In the IASNP system, firing mechanism, fuzzy logic, new types of neurons and a matrix algorithm are introduced. The IASNP system is used to model the distribution networks while its matrix algorithm locates faults by considering the causality between regions and the associated nodes. Finally, two cases, including a multi source distribution network and distribution network with distributed generation, are used to verify the validity and accuracy of the proposed method.