Showing papers by "Jun Wang published in 2010"
TL;DR: It is proved that integers suffice for computing all Turing computable sets of numbers in both the generative and the accepting modes and a characterization of the family of semilinear sets ofNumbers is obtained.
Abstract: A variant of spiking neural P systems with positive or negative weights on synapses is introduced, where the rules of a neuron fire when the potential of that neuron equals a given value. The involved values---weights, firing thresholds, potential consumed by each rule---can be real (computable) numbers, rational numbers, integers, and natural numbers. The power of the obtained systems is investigated. For instance, it is proved that integers (very restricted: 1, -1 for weights, 1 and 2 for firing thresholds, and as parameters in the rules) suffice for computing all Turing computable sets of numbers in both the generative and the accepting modes. When only natural numbers are used, a characterization of the family of semilinear sets of numbers is obtained. It is shown that spiking neural P systems with weights can efficiently solve computationally hard problems in a nondeterministic way. Some open problems and suggestions for further research are formulated.
133 citations
TL;DR: A novel image watermarking method in multiwavelet domain based on support vector machines (SVMs) is proposed in this paper, which can more effectively reduce image distortion than that of conventional single coefficient.
85 citations
24 Aug 2010
TL;DR: It is proved that a uniform family of SN P systems with neuron division can efficiently solve SAT in a deterministic way, not using budding, while additionally limiting the initial size of the system to a constant number of neurons.
Abstract: Spiking neural P systems (SN P systems, for short) are a class of distributed parallel computing devices inspired from the way neurons communicate by means of spikes. The features of neuron division and neuron budding are recently introduced into the framework of SN P systems, and it was shown that SN P systems with neuron division and neuron budding can efficiently solve computationally hard problems. In this work, the computation power of SN P systems with neuron division only, without budding, is investigated; it is proved that a uniform family of SN P systems with neuron division can efficiently solve SAT in a deterministic way, not using budding, while additionally limiting the initial size of the system to a constant number of neurons. This answers an open problem formulated by Pan et al.
20 citations
29 Nov 2010
TL;DR: This paper presents a fuzzy spiking neural P system (FSN P system) to represent the fuzzy production rules in a knowledge base of a rule-based system, where the certainty factors of fuzzyproduction rules and the truth values of propositions are described by trapezoidal fuzzy numbers.
Abstract: This paper presents a fuzzy spiking neural P system (FSN P system) to represent the fuzzy production rules in a knowledge base of a rule-based system, where the certainty factors of fuzzy production rules and the truth values of propositions are described by trapezoidal fuzzy numbers. In the proposed FSN P system, the definition of traditional neurons has been extended. The neurons are divided into two types: proposition neurons and rule neurons; the content of each neuron is a trapezoidal fuzzy number in [0, 1] instead of an integer. Also the fuzzy reasoning process can be modeled by the proposed FSN P system.
7 citations
29 Nov 2010
TL;DR: A new firing principle is introduced into the timed spiking neural P systems instead of original firing and delay mechanisms in spiking Neural P systems, which can effectively represent both qualitative and quantitative temporal information.
Abstract: In this paper, we present a new class of spiking neural P systems for handling temporal information and representing temporal knowledge, called timed spiking neural P systems. A new firing principle is introduced into the timed spiking neural P systems instead of original firing and delay mechanisms in spiking neural P systems. The timed spiking neural P systems can effectively represent both qualitative and quantitative temporal information.
3 citations