Spiking neural P systems are a new computing model inspired from the biological phenomena that in the brain the neurons cooperate to deal with spikes by axons. Since it has been shown that they have powerful computati...
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Spiking neural P systems are a new computing model inspired from the biological phenomena that in the brain the neurons cooperate to deal with spikes by axons. Since it has been shown that they have powerful computational capability and potential capability in solving computationally hard problems, more and more people begin to get interested in this field. This paper firstly introduces the formal definition of standard spiking neural P systems and some notions which are often used in this area;then, several extensions of the original spiking neural P systems are summarized, that are: Extented SN P system;SN P system with exhaustive use of rules;Asynchronous SN P system;Sequential SN P system. Also, the results on the topic of spiking neural P systems are briefly recalled in two aspects: computational completeness and computational efficiency. In the end, two more important future research directions on spiking neural P systems are pointed out. Specifically, one interesting topic is to develop a new computing model which is more "realistic";another topic is to consider how to use these models in biological modeling and simulation.
Supply chain is a complex system. The complexity of supply chain can be categorized into two kinds: the complexity of supply chain components as well as the complexity of the supply chain organizations, and both infor...
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Supply chain is a complex system. The complexity of supply chain can be categorized into two kinds: the complexity of supply chain components as well as the complexity of the supply chain organizations, and both information uncertainty with system dynamics are the main reasons that lead to supply chain complexity. Concerning supply chain complexity, we review the related researching approaches in the literature from uncertainty, the measure of supply chain complexity as well as the dynamic analysis of the supply chain (including dynamic games), and give suggestions in the future research.
The paper proposes a shape-adaptive wavelet coding algorithm for the known object of the diagnostic region of three-dimensional medical images. The new algorithm only applies to the shape-adaptive transformation of th...
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The paper proposes a shape-adaptive wavelet coding algorithm for the known object of the diagnostic region of three-dimensional medical images. The new algorithm only applies to the shape-adaptive transformation of the pixels inside the object for decorrelation. After transformation, the number of coefficients of the object is as many as that of the pixels inside the image area. To achieve a quick and lossless transformation, a novel shape-adaptive wavelet transform based on lifting scheme for arbitrarily shaped object is proposed. By analyzing the location of invalid coefficients transformed, the paper also proposes a modified OB-3DSPECK (Object-based Set Partitioned Embedded Block Coder) method that cancels symbol outputs of invalid block or coefficients outside the object, specifically, only two types of symbols are output to arithmetic coding codec. For the object region of three-dimensional medical images, the proposed algorithm supports the lossy-to-lossless embedded en/decoding. Experimental results show that the proposed algorithm outperforms OB-3DSPIHT by 0.5 dB on the average SNR. Furthermore, because of the reduction of the output of one type symbol, the arithmetic coding becomes optional.
A multi-agent social evolutionary algorithm for the precedence and resource constrained single-mode project optimization scheduling (RCPSP-MASEA) is proposed. RCPSP-MASEEA is used to obtain the optimal scheduling sequ...
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A multi-agent social evolutionary algorithm for the precedence and resource constrained single-mode project optimization scheduling (RCPSP-MASEA) is proposed. RCPSP-MASEEA is used to obtain the optimal scheduling sequences so that the duration of the project is minimized. With the intrinsic properties of RCPSP in mind, the multi-agent systems, social acquaintance net and evolutionary algorithms are integrated to form a new algorithm. In this algorithm, all agents live in lattice-like environment. Making use of the designed behaviors, RCPSP-MASEA realizes the ability of agents to sense and act on the environment in which they live, and the local environments of all the agents are constructed by social acquaintance net. Based on the characteristics of project optimization scheduling, the encoding of solution, the operators such as competitive, crossover and self-learning are given. During the process of interacting with the environment and the other agents, each agent increases energy as much as possible, so that RCPSP-MASEA can find the optima. Through a thorough computational study for a standard set of project instances in PSPLIB, the performance of algorithm is analyzed. The experimental results show RCPSP-MASEA has a good performance and it can reach near-optimal solutions in reasonable times. Compared with other heuristic algorithms, RCPSP-MASEA also has some advantages.
Dezert-smarandache theory (DSmT) was extended with fuzzy theory by considering the different Fuzzy T-norm operators, in order to develop a more general and flexible combinational rule for more extensive application. A...
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Dezert-smarandache theory (DSmT) was extended with fuzzy theory by considering the different Fuzzy T-norm operators, in order to develop a more general and flexible combinational rule for more extensive application. At the same time, fuzzy-extended DSmT was applied to mobile robot's sensing environment with the help of new self-localization method based on δ neighboring field appearance matching and also the perception effect was compared with different T-norm operators. Finally, an effective approach to solv sensing fusion of uncertainty environment was found.
This paper presents a sliding-mode-based diagonal recurrent cerebellar model articulation controller (SDRCMAC) for multiple-input-multiple-output (MIMO) uncertain nonlinear systems. Sliding mode technology is used to ...
This paper presents a sliding-mode-based diagonal recurrent cerebellar model articulation controller (SDRCMAC) for multiple-input-multiple-output (MIMO) uncertain nonlinear systems. Sliding mode technology is used to reduce the dimension of the control system. Two learning stages are adopted to train the SDRCMAC and to improve the stability of the control system. Lyapunov stability theorem and Barbalat's lemma are adopted to guarantee the asymptotical stability of the system. Performance is illustrated on a two-link robotic control and motor control of the human arm in the sagittal plane.
In this paper, a variant of support vector novelty detection (SVND) with dot product kernels is presented for non-spherical distributed data. Firstly we map the data in input space into a reproducing kernel Hilbert sp...
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In this paper, a variant of support vector novelty detection (SVND) with dot product kernels is presented for non-spherical distributed data. Firstly we map the data in input space into a reproducing kernel Hilbert space (RKHS) by using kernel trick. Secondly we perform whitening process on the mapped data using kernel principal component analysis (KPCA). Finally, we adopt SVND method to train and test whitened data. Experiments were performed on artificial and real-world data.
To overcome the main drawbacks of global minimal for active contour models (L. D. Cohen and Ron Kimmel) that the contour is only extracted partially for low SNR images, Method of boundary extraction based on Schrö...
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To overcome the main drawbacks of global minimal for active contour models (L. D. Cohen and Ron Kimmel) that the contour is only extracted partially for low SNR images, Method of boundary extraction based on Schrödinger Equation is proposed. Our Method is based on computing the numerical solutions of initial value problem for second order nonlinear Schrödinger equation by using discrete Fourier Transformation. Schrödinger transformation of image is first given. We compute the probability P(b,a) that a particle moves from a point a to another point b according to I-Type Schrödinger transformation of image and obtain boundary of object by using quantum contour model.
We consider the problem of looking for small universal spiking neural P systems with exhaustive use of rules, which was formulated as an open problem by Gheorghe Paun in a survey paper. Here, spiking neural P systems ...
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We consider the problem of looking for small universal spiking neural P systems with exhaustive use of rules, which was formulated as an open problem by Gheorghe Paun in a survey paper. Here, spiking neural P systems are used in two versions: as devices computing functions and as devices generating sets of numbers, with two ways of encoding the result of a computation. As devices of computing functions, if we associate the result with the distance between the first two spikes emitted by the output neuron, we produce a universal computing spiking neural P system with exhaustive use of rules (without delay) having 125 neurons; if we introduce the usual way of defining the result of a computation in membrane systems to encode the result, namely, the number of spikes emitted during a computation, then a universal computing system (without delay) with 126 neurons is also obtained in the sense of the exhaustive use of rules. For spiking neural P systems used as generators of sets of numbers, we construct a universal system (without delay) by using 128 neurons under the first way of defining the computation result, and a system (without delay) by using 127 neurons under the second way of defining the computation result.
In this paper, based on Baldwin effect, an improved clonal selection algorithm, Baldwin clonal selection algorithm, termed as BCSA, is proposed to deal with complex multimodal optimization problems. BCSA evolves and i...
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In this paper, based on Baldwin effect, an improved clonal selection algorithm, Baldwin clonal selection algorithm, termed as BCSA, is proposed to deal with complex multimodal optimization problems. BCSA evolves and improves antibody population by three operations: clonal proliferation operation, Baldwinian learning operation and clonal selection operation. By introducing Baldwin effect, BCSA can make the most of experience of antibodies, accelerate the convergence, and obtain the global optimization quickly. In experiments, BCSA is tested on four types of functions and compared with the clonal selection algorithm and other optimization methods. Experimental results indicate that BCSA achieves a good performance, and is also an effective and robust technique for optimization.
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