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Batch processes have the characteristic of more operation phases in nature. The standard Fuzzy C-Means (FCM) algorithm for phase partition of batch processes needs to a given phase partition number beforehand, initialize clustering centers randomly, and is sensitive to noise and outliers. For the above problems, the adaptive FCM algorithm using clustering validity function is proposed to achieve the adaptive partition of batch process operation phases. The method obtains the initial clustering center set on the basis of maximum minimum distance rule, through adaptive iteration way determines the optimal clustering number by introducing the clustering validity function. The MICA model based on the improved phase partition method is applied to fault detection of industrial penicillin fermentation process and the experimental results verify the effectiveness of the proposed method.

Relative growth is defined as the description of the growth relationship between biological and biological part (organ). The relative growth function is a function on variable of growth and non-time variables. In the paper, a system relative growth model was constructed by the means of relatives the average height, the stand basal area at breast height, height-diameter curve, stem curve, stand density and the growth of diameter class basal area. The system model was constructed in order to predict the growth of the stand and the diameter. The results show that the model had the advantages of generality which was an effective method to predict the growth of different stand. The relative growth model is an effective method to predict the growth of stand.

In this experiment, by using the method of artificial neural network and DPS DATA PROCESSING SYSTEM combined with the meteorological data of air temperature, relative air humidity, solar radiation, wind speed, soil water content and dew point temperature as the input variable, the author established the artificial neural network system to forecast the seedling water consumption of P.×euramericana cv.“74/76”, and through the experiments it has been examined that three neural network system models can be applied in forecasting water consumption of seedlings, and the average relative error of Back Propagation (BP) neural network prediction model was 0.04, the projection pursuit regression (PPR) neural network prediction model was 0.03, the multiple stepwise regression anatomic model was 0.10, moreover, the latter two ones had good stability, while that of BP neural network prediction model was poor. Therefore, we propose that PPR neural network model can be used in prediction of seedling water consumption. Furthermore, the maximum relative error of PPR neural network predication model was 0.073, the minimum relative error was 0.002. The neural network model is superior to the former linear model that the neural network model performs a higher forecasting accuracy with relatively shorter time consumption in training.

This paper introduce the theory of the adaptive noise canceling system and LMS algorithm, also present the emulational chart of adaptive filter based on LMS algorithm. Analysis of the emulational result indicate the adaptive noise canceling technic can effectively cancel interferential sine wave, so in the disposal of sound signal can eliminate background noise and achieve the purpose of advancing sound signal quality.

Based on MATLAB programming platform, this paper carried out the WVAR numerical solution according to the Simpson formula, meanwhile established a portfolio model based on WVAR. Study proves that when volatility of the stock market is flat, the risk characterized by WVAR on the U.S. Nasdaq index has more advantages over the VAR and TVAR.

Forest harvesting operation is based on Logging design, scientific harvesting design can reduce the negative impacts of logging activities on forest ecosystems. This article based on the C# language of NET technology, combining with the technical regulation of ecological Logging, management of control method and growth model of natural forest, developed the user-oriented Export Supporting System for Ecological Logging of Natural Forest. This system can assist user to achieve intelligent design of logging, determine the felling quantity, configuration of felling areas and visualization. It forms automatic processing flow and assists the design of ecological logging.

Water consumption of plants is a key parameter for formulating irrigation system, and the precise prediction play a important role in improving the use efficiency of limited water resources. In this experiment, by using the method of artificial neural network and MATLAB DATA PROCESSING SYSTEM combined with the meteorological data of air temperature, relative air humidity, solar radiation, wind speed, soil water content and dew point temperature as the input variable, the author established the artificial neural network system to forecast the seedling water consumption of P.×euramericana cv.“74/76”, and through the experiments it has been examined that two neural network system models can be applied in forecasting water consumption of seedlings, and the average relative error of Back Propagation (BP) neural network prediction model was 0.07, the General Regression Neural Network (GRNN) prediction model was 0.05, moreover, the latter had good stability, while that of the former was poor. Therefore, we propose that GRNN model can be used in prediction of seedling water consumption. Furthermore, the maximum relative error of GRNN predication model was 0.106, the minimum relative error was 0.015. The GRNN model is superior to the BP neural network model that the former performs a higher forecasting accuracy with relatively shorter time consumption and faster speed in training.

Relative growth is defined as the description of the growth relationship between biological and biological part (organ). The relative growth function is a function on variable of growth and non-time variables. In the paper, a system relative growth model which taken Betula costata as an example was constructed based on the projection pursuit regression (PPR) artificial neural network. The system model was constructed in order to predict the tree growth. The results show that the artificial neural network model were available for prediction of the tree growth; the predication average relative error of diameter growth was 0.04, the predication average relative error of height was 0.06,the predication average relative error of volume was 0.12. The PPR model was available for tree growth predication which can be applied to predict the tree growth.

In order to solve the problem of automatic division of forest category in forest management, this article based on the C# language of NET technology, combining with the forest division regulation and the method of comprehensive factor evaluation, established a set of forest species division indicator system and developed the divisions of forest category system by the component of ArcGIS Engine. This system can auxiliary realizing functions of automatic forest category division, mapmaking, query and statistics. Comparing with the traditional method, this method is more convenient and accurate, reducing man-made error and improving work efficiency.