Artificial neural networks to estimate the productivity of soybeans and corn by chlorophyll readings

Crop productivity prediction techniques assist with adjusting for potential agronomic problems during the growing season. Several authors have reported that there is a correlation between leaf chlorophyll (Chl) content and yield. This study developed independent artificial neural network (ANN) models for soybean and corn in order to predict the crops' productive potentials using their respective yields and leaf Chl content data, measured at three stages of plant development. The ANN was deemed ready for testing through verification of the mean squared error and the number of epochs while training the neural network. While the model obtained when Chl was measured in the V6 stage of development explained more than 50% of the productivity data in corn, the models obtained for soybean did not explain more than 10% of the observed data. Attempts to improve the model through changes of the architecture of the neural network did not show any improvement in model.     

AN INEXACT REASONING INFORMATION MANAGEMENT SYSTEM

The author introduces some basic ideas and methods of artificial intelligence into an information management system,and therefore proposes a design for the information management system which has the ability of inexact reasoning based on the certainty knowledge. According to the design, the system can also be made with the flexibility of change and expansion, and good adaptability for application. This paper describles the design and its programing skill in detail.     

AN EXPERT TYPE CONTROLLER FOR INDUSTRIAL PROCESS

In this paper an expert system concept in AI is introduced to the industrial process control. Specific structures and features of expert control system are discussed. An expert controller directly applicable to industrial process is designed. The simulation of control system indicates that the expert controller has strong information precssing capability and versatile control strategies with desirable control performance. Thus, expert control approach is an effective means towards the realization of intelligent Control.     

农业生产计划模拟退火算法应用

针对有些企业生产计划编制问题，提出了基于模拟退火算法(SA)的生产计划算法，并给出了生产计划算法的描述，依据实验数据分析了生产计划算法的性能。     

智能开关技术的应用

随着科学技术的进步,我国的电力电子技术和电子器件都有了很大的发展.智能开关技术的发展,从根本上解决了传统电器启动的额定电流高易造成过热引起高温而加速 绝缘老化、电压降过大而影响正常运转以及大的冲击力缩短设备的使用寿命等各种弊端.为此,论述了智能开关的功能、特点及应用,旨在此技术得以广泛应用.     

BP神经网络最优组合预测方法及其应用

为了解决各种单一传统的预测方法中存在的问题，提出了一种BP神经网络最优组合的预测方法。将单一预测方法所得到的预测值作为BP神经网络的输入样本，相应历史数据的实际值作为样本的输出，经过样本训练达到期望精度，应用BP神经网络进行预测。通过对浙江省农机总动力需求预测，表明该方法比各种单一的预测方法都有更高的精度。     

基于神经网络的传感器非线性校正方法

BP神经网络是基于数值计算的知识处理系统，具备高度非线性映射的能力。为此，通过BP神经网络对样本数据进行训练，得到非线性校正模型及精度值，然后与传统的曲线拟合方法进行比较分析。结果显示，基于神经网络模型的传感器非线性校正方法具有良好的测试精度和实用性。     

蚁群算法在机械优化设计中的应用

介绍了蚁群算法的原理、模型和算法实现过程，并采用该算法对机械优化的一个实例：对压缩圆柱螺旋弹簧的优化设计模型进行了优化设计，优化计算结果表明。基于蚁群算法的优化设计切实可行，为复杂的机械优化设计提供了新的思路和方法。     

基于网络的农产品精细加工技术体系初探

针对农产品精细加工技术体系的不完善，在充分考虑到我国农产品加工业目前面临的一些主要形势和任务，综合运用网络技术、可视化技术、人工智能、计算机、自动控制、现代管理、先进加工技术、农产品加工等多学科知识，提出了基于网络的可视化、集成化、智能化农产品精细加工技术体系，并对该体系中的可视化、集成化、智能化以及柔性加工和敏捷加工等先进技术进行了概括的阐述。同时对该体系的建立与实施提出了科学务实的措施。该体系的建立与实施有助于解决目前我国农产品加工业存在的一些主要问题。     

NEPER: a multiple strategy wheat expert system

This paper presents a methodology for developing a strategic Expert System for wheat crop production, which has been developed using a Multiple Design approach. This Expert System consists of six subsystems; namely, variety selection, land preparation, sowing, irrigation, fertilization, and harvest. These subsystems exchange data between them through a common database. Each subsystem advises wheat growers on the corresponding plan(s), comprising a set of agricultural/chemical operations before or during the growing season. Applying an appropriate plan reduces risk to farmers from having a problem during the growing season. The system was verified, validated, and, tested in the field. Beneficial economic and environmental impacts have been demonstrated.