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Particle swarm optimization (PSO) as a novel intelligent optimization algorithm has been used successfully in many fields, but its application to flood hazard risk assessment is a new research topic. This paper introduces the theory and flow of application of particle swarm optimization rule mining (PSO-Miner) algorithm to flood damage risk assessment. This paper selected Beijiang River Basin, China, as study area for flood damage risk assessment based on PSO-Miner algorithm and BPANN method. The results of a case study indicate that the advantages of PSO-Miner algorithm can be summarized as follows: It does not assume an implicit assumption for processing dataset and has strong robustness; it can mine very simple assessment rules; it can have a better performance than BPANN model. So the PSO-Miner algorithm provides a new approach for flood risk assessment. [ABSTRACT FROM AUTHOR]

In this paper a fuzzy portfolio selection optimization decision based on a combination of forecasting model is constructed, representative selection of the genetic neural network model, multi-factor SVM regression model and ARIMA time series model as a combination of a single model for forecasting. This paper establishes possibilistic mean-variance-entropy portfolio selection model in which a single model to predict the result is treated as fuzzy variable, the empirical results show that the possibilistic mean-variance-entropy portfolio selection model can obtain higher yield and lower relative risk. The methodology of this paper is useful in such practice as fund management, financial risk management. It is hoped that the methodology can improve the scientific level of decision making. [ABSTRACT FROM AUTHOR]

This paper proposes a new semi-parametric STAR model in which we allow transition variables to enter into the transition function nonparametrically but the transition function itself is a known parametric form. The new model can avoid the risk of misspecification and improve the in-sample goodness of fit and the out-of-sample forecasts. Monte Carlo simulations show that the estimates have good finite sample performance. Using the monthly real effective exchange rates from January 1994 to July 2012, we find that the new model outperforms other popular models such as the random walk model, the autoregressive model, the threshold autoregressive model, the smooth transition autoregressive model and artificial neural network model in terms of out-of-sample forecasting ability. [ABSTRACT FROM AUTHOR]

The development and utilization of new energy is an important way to solve the energy shortage, environmental pollution and so on, which is also an effective power source for the development of low carbon economy. This paper starts with nonlinear dynamics, brings new energy into the dynamics system of energy-saving and emission-reduction and analyzes the effect of new energy on energy intensity and economic growth. Based on Lyapunov exponents and bifurcation diagrams, the dynamic behavior of the system is analyzed, energy-saving and emission-reduction attractor with new energy constraints is put forward. Artificial neural network is used to identify the quantitative coefficients in the simulation models according to the statistical data of China, and an empirical analysis of the real system is carried out with the results in perfect agreement with actual situation. The result shows that, by relying on the development of new energy or simply increasing investment to develop new energy, the effect of controlling energy intensity is not conspicuous. When the financial investment is too high, the inhibition effect on economic growth is great or fatal. Increasing overall investment in energy-saving and emission-reduction system which includes new energy, energy intensity could be declined better. When the overall investment gets larger, the inhibition effect on economic growth is also getting bigger in the early stage, however, with the further development of the dynamics system and the gradual maturity of new energy, the investment's promoting effect on economic growth is also very bigger. Numerical simulations are carried out to demonstrate the results. [ABSTRACT FROM AUTHOR]