Fuzzy nonlinear regression analysis using a random weight network

Analysis of GDP based on polynomial regression and BP neural network

GDP(Gross Domestic Product) is the important index for measuring economic development. Quantitative analysis and prediction for GDP can regulate the economic development trend and promote steady economic development. In this article, we used Jiangsu province as an example. Firstly, we determined to 19 indexes which could explain GDP potentially by national economic accounting theory, and a quadratic polynomial regression model was used between the GDP and the principal factor. Secondly, in order to efficiently predict the GDP, we established a BP neural network model with excellent parameter taking 19 indexes as independent variable and GDP as dependent variable. Finally, we came to the conclusion about the GDP of Jiangsu province, and some suggestions for development were given.

Comparative assessments of multivariate nonlinear fuzzy regression techniques for egg production curve

The modelling process of egg production curves, where environmental and genetic factors are highly effective, is quite complex and difficult. In particular, the limitations of measurement and the errors encountered during the measurement process may cause uncertainty in the egg production process. In this study, multivariate nonlinear fuzzy regression analysis was used by configuring neural networks and least squares support vector machines in order to express the uncertainty in the system structure during the egg production process. This method was used to obtain the predicted values for egg production in the fuzzy frame. In the study, two different data sets were used which were measured for egg performance and egg weight variables in daily and weekly time periods. Multivariate nonlinear fuzzy regression analysis results were compared with both the observed values and the multivariate classical regression analysis results. Results of analysis show that multivariate nonlinear fuzzy regression analysis with neural networks is more successful than other methods and can be used as an alternative to classical methods in poultry farming.

Improved fuzzy PID controller design using predictive functional control structure

In conventional PID scheme, the ensemble control performance may be unsatisfactory due to limited degrees of freedom under various kinds of uncertainty. To overcome this disadvantage, a novel PID control method that inherits the advantages of fuzzy PID control and the predictive functional control (PFC) is presented and further verified on the temperature model of a coke furnace. Based on the framework of PFC, the prediction of the future process behavior is first obtained using the current process input signal. Then, the fuzzy PID control based on the multi-step prediction is introduced to acquire the optimal control law. Finally, the case study on a temperature model of a coke furnace shows the effectiveness of the fuzzy PID control scheme when compared with conventional PID control and fuzzy self-adaptive PID control.