Showing papers by "Gexiang Zhang published in 2009"

An improved membrane algorithm for solving time-frequency atom decomposition

Abstract: To decrease the computational complexity and improve the search capability of quantum-inspired evolutionary algorithm based on P systems (QEPS), a real-observation QEPS (RQEPS) was proposed RQEPS is a hybrid algorithm combining the framework and evolution rules of P systems with active membranes and real-observation quantum-inspired evolutionary algorithm (QEA) The RQEPS involves a dynamic structure including membrane fusion and division The membrane fusion is helpful to enhance the information communication among individuals and the membrane division is beneficial to reduce the computational complexity An NP-complete problem, the time-frequency atom decomposition of noised radar emitter signals, is employed to test the effectiveness and practical capabilities of the RQEPS The experimental results show that RQEPS is superior to QEPS, the greedy algorithm and binary-observation QEA in terms of search capability and computational complexity

Solving satisfiability problems with membrane algorithms

Abstract: This paper presents the application of membrane algorithms to satisfiability problems which are well-known NP-hard combinatorial optimization problems. The membrane algorithm, called QEPS, is a combination of P system approaches and quantum-inspired evolutionary algorithms. QEPS employs the hierarchical structure of the compartments of P systems, the objects consisting of quantum-inspired bit individuals, the rules composed of quantum-inspired gate evolutionary rules and transformation/communication-like rules in P systems to specify the membrane algorithms. A large number of experiments carried out on bench satisfiability problems show that QEPS performs better than its counterpart quantum-inspired evolutionary algorithm.

A Memetic Algorithm Based on P Systems for IIR Digital Filter Design

Abstract: To improve the local search capability of quantum-inspired evolutionary algorithm based on P systems (QEPS), a memetic algorithm based on P systems (MAPS) was proposed. MAPS is a hybrid algorithm combining the hierarchical framework and evolution rules of P systems with real-observation quantum-inspired evolutionary algorithms (rQIEA) and local search methods (LS). In MAPS, rQIEA is employed in elementary membranes to explore the whole solution space and TS is applied inside the skin membrane to search the neighbouring domains of each variable of the best solution obtained. Five complex benchmark functions with 100 dimensions are employed to test the effectiveness of the approach. Experimental results show that MAPS performs better than rQIEA in terms of search ability and stability. In addition, this paper presents the application of membrane algorithms to infinite-impulse response (IIR) digital filter design. The experiments show that MAPS can obtain better digital filter performances than NQGA and GA.

A quantum-inspired evolutionary algorithm based on P systems for radar emitter signals

Abstract: In this paper, a quantum-inspired evolutionary algorithm based on P systems (QEPS) is used for radar emitter signals to promote the application of membrane computing. QEPS combines the framework and evolution rules of P systems with the chromosome representation and evolutionary mechanism of quantum-inspired evolutionary algorithms (QIEA). With good global search capability and rapid convergence, QEPS can extract specific information from radar emitter signals in a short span of time. Experiments are carried out on linear-frequency modulated radar emitter signals with 10 db signal-to-noise rate to test the effectiveness and practicality of the introduced approach. Experimental results show that QEPS performs better than the greedy algorithm and the counterpart QIEA.

Feature Extraction of Radar Emitter Harmonic Power Constraint Based on Nonlinear Characters of the Amplifier

Abstract: In radar countermeasures systems, because each emitter has its own electromagnetic properties inside its transmitted signal, the specific emitter can be identified using received radar signals. Traditionally, the specific emitter identification (SEI) depends on analyzing the time-frequency structure within the usage band. In this paper, a new SEI feature extraction approach based on autocorrelation analysis is proposed. To characterize the nonlinearity of the amplifier in the transmitter, the harmonic power constraint characteristic of the output signal of the amplifier is analyzed, and a power series model is applied to describe the output signal. For different amplifiers, harmonic power constraint properties are different. We present the autocorrelation analysis method to estimate each harmonic power spectrum and extract harmonic power ratio signature features from output signal of the amplifier. The validity of the proposed method is confirmed by simulation experiments. Keywords-radar emitter; harmonic power; amplifier nonlinearity; feature extraction

Tuning p systems for solving the broadcasting problem

Abstract: P systems are employed in various contexts to specify or model different problems. In certain cases the system is not entirely known. In this paper we will consider the broadcasting algorithm and present a method to determine the format of the rules of the P system used to specify the algorithm.

Improved Differential Evolutions Using a Dynamic Differential Factor and Population Diversity

Abstract: As a new kind of evolutionary algorithms, differential evolution (DE) has attracted much attention in solving optimization problems in the last few years. To accelerate its convergence rate and enhance its performances, this paper introduces a dynamic adjustment method for the differential factor and a modified version of mutation strategy into DE. Furthermore, a disturbance approach based on population diversity is used to further improve the search capability. Thus, two improved DE, IDE1 and IDE2, are presented. The performances of the IDE1 and IDE2 are evaluated on seven complex benchmark functions with three different dimensionalities. Experimental results show that the performances of IDE1 and IDE2 are superior to other two DEs in terms of convergence rates and qualities of solutions.

A Differential Evolution Based Time-Frequency Atom Decomposition for Analyzing Emitter signals

Abstract: This paper discusses the use of time-frequency atom decomposition based on a differential evolution to analyze radar emitter signals Decomposing a signal into an appropriate time-frequency atoms is a well-known NP-hard problem This paper applies a differential evolution to replace the traditional approach, a greedy strategy, to approximately solve this problem within a tolerable time A large number of experiments conducted on various radar emitter signals verify the feasibilities that the time-frequency characteristics are shown by using a small number of decomposed time-frequency atoms, instead of traditional time-frequency distributions