Hengqing Tong

Evaluation model and its iterative algorithm by alternating projection

This paper proposes a kind of evaluation model. It is a linear model in which both y and @b are variable and with linear convex constraint. The paper also obtains the models solution and discusses its properties. Also, an iterative computation method is proposed that makes use of alternating projection between two convex sets.

Best iterative initial values for PLS in a CSI model

According to the characters of customer satisfaction index (CSI) models, we transformed these models into common regression models, and we find suitable iterative initial values with the constraint of a unit vector for latent variables. The convergence of the new algorithm is also illustrated in this paper. Consequently, the partial least square (PLS) algorithm for CSI models is improved greatly with the best iterative initial values. The results of this paper have been embodied into the software DASC.

Dynamic shortest path in stochastic traffic networks based on fluid neural network and Particle Swarm Optimization

The shortest path algorithm is critical for dynamic traffic assignment and for the realization of route guidance in intelligent transportation systems (ITS). In this paper, a hybrid Particle Swarm Optimization (PSO) algorithm combined fluid neural network (FNN) to search for the shortest path in stochastic traffic networks is introduced. The algorithm overcomes the weight coefficient symmetry restrictions of the traditional FNN and disadvantage of easily getting into a local optimum for PSO algorithm. Simulation experiments have been carried out on different traffic network topologies consisting of 15–70 nodes and the results showed that the proposed approach can find the optimal path with good success rates and also can find closer sub-optimal paths with high success ratio for all the tested traffic networks. At the same time, the hybrid algorithms improve greatly the efficiency of the convergence of the fluid neuron network, and decrease the computation time of optimization path.

Dynamic Framed Slotted ALOHA Algorithm Based on Bayesian Estimation in RFID System

In RFID system, one of the key problems is the collision between tags which lowers the throughput of the system. The Dynamic Framed Slotted ALOHA is a widely used algorithm in the anti-collision field of RFID system. In this paper, we propose a Dynamic Framed Slotted ALOHA Algorithm based on Bayesian Estimation. Compared to the conventional algorithms, our algorithm takes the advantage of the evidence in previous frames as the priori information of the current frame which leads to the precise estimation of the tag number and rational frame length adjustment. The simulation results show that our algorithm can improve the average throughput of the RFID system and reduce the total slots used to identify tags.

The Hardware/Software Partitioning in Embedded System by Improved Particle Swarm Optimization Algorithm

Hardware/software partitioning is a key problem in hardware/software co-design. This paper presents a new hardware/software partitioning methodology based on improved particle swarm optimization algorithm. The model of the embedded system was constructed by directed acyclic graph to obtain the objective function. Then improvement strategies are introduced in order to overcome the problems of low precision and divergence in traditional particle swarm optimization algorithm. The improved algorithm can avoid local optimal solution efficiently and be conveniently implemented in the field of hardware/software partitioning.

A definite linear algorithm for structural equation model

A definite linear algorithm for structural equation model (SEM) is proposed that is developed from modular constraint (the length of vector is 1) least squares (MCLS) solution of SEM which may be used as the best iterative initial value of partial least square (PLS) algorithm. Based on MCLS, prescription regression (all regression coefficients are nonnegative and their sum is 1), and generalized linear regression model (the dependent variable is unknown), a definite linear algorithm is constructed. The data example of SEM with multilayer construction is provided to illustrate the algorithm.

Self-organized path constraint neural network: structure and algorithm

Due to its flexibility and self-determination, self-organized learning neural network(NN) has been widely applied in many fields. Meanwhile, it has a well trend to develop. In our research, we find that structural equation modeling (SEM) may be reconstructed into a self-organized learning neural network, but the algorithm of NN need to be improved. In this paper, we first present an improved partial least square (PLS) algorithm in SEM using a suitable iterative initial value with constraint of unit vector. Then we propose a new self-organized path constraint neural network(SPCNN) based on SEM. Furthermore, we give the topology structure of SPCNN, describe the learning algorithm of SPCNN, including common algorithm and algorithm with a suitable initial weights value, and elaborate the function of SPCNN.

Unsupervised learning neural network with convex constraint: Structure and algorithm

This paper proposes a kind of unsupervised learning neural network model, which has special structure and can realize an evaluation and classification of many groups by the compression of data and the reduction of dimension. The main characteristics of the samples are learned after being trained. In order to realize unsupervised learning of neural network structure with convex constraint, an iterative computation method is proposed that makes use of alternating projection between two convex sets. The final example proves that this method can detect instructions without a mass of supervised data and it converges fast.

Control a Novel Discrete Chaotic System through Particle Swarm Optimization

To investigate the inherent chaotic phenomenon in genetic algorithms (GA), a novel chaos control approach through particle swarm optimization (PSO) (CCPSO) with three main processes is proposed. Firstly it detects the dynamical behaviors of a new discrete chaotic system with rational fraction in GA such as its unstable periodic points. Then it directs the chaotic system to its unstable fix point from any initial point by global controlling factors {Uk} solved self-adaptively by PSO. Thirdly a multi-model solution for chaos control with double plasticity of parameter and structure generated by PSO is proposed to stabilize the system on its unstable fix point. And finally details of applying the proposed method into CCPSO are given, and experiments done show the put approachs effectiveness

UEAS: a novel united evolutionary algorithm scheme

How to detect global optimums of the complex function is of vital importance in diverse scientific fields. Though stochastic optimization strategies simulating evolution process are proved to be valuable tools, the balance between exploitation and exploration of which is difficult to be maintained. In this paper, some established techniques to improve the performance of evolutionary computation are discussed firstly, such as uniform design, deflection and stretching the objective function, and space contraction. Then a novel scheme of evolutionary algorithms is proposed to solving the optimization problems through adding evolution operations to the searching space contracted regularly with these techniques. A typical evolution algorithm differential evolution is chosen to exhibit the new schemes performance and the experiments done to minimize the benchmark nonlinear optimization problems and to detect nonlinear maps unstable periodic points show the put approach is very robust.