SBIR Phase I: A Hybrid Neural Net Enterprise System Simulation

小型企业创新研究第一阶段项目旨在解决《扩展企业》/C.2中规定的管理和模拟扩展企业系统的问题。随着网络和企业系统变得越来越复杂，确定部署硬件以最大化系统可用性和网络性能的最具成本效益的方法变得越来越困难。糟糕的资源规划降低了信息技术组织的效率，导致劳动力成本增加，降低了整个公司的生产率。需要新的工具来提高生产率。这项建议是为了研究产生一个建模和预测软件系统，使用神经网络和基于规则的算法相结合来模拟企业系统。企业系统的各个组成部分将用由建设性技术训练的适当神经网络进行建模。通过将这些组件神经网络与基于数据管道规则的软件连接起来，对企业系统进行建模。这样的软件将能够预测系统上的压力，并识别网络中的瓶颈。完成的建模和预测软件将成为诊断、故障排除和预测IT问题的工具的基石技术。该项目将使用C、C++或FORTRAN在Linux上开发。这项研究将有助于提高企业系统和网络的管理效率，从而使教学和服务机构更容易负担得起。这一努力的结果将帮助企业系统和网络管理人员以更快和更经济的方式管理任务。

This Small Business Innovation Research Phase 1 project is designed to address the problem of managing and simulating extended enterprise systems as specified in The Extended Enterprise/C.2. As networks and enterprise systems have grown in complexity, it has become increasingly difficult to determine the most cost effective way to deploy hardware to maximize system availability and network performance. Poor resource planning decreases the effectiveness of information technology organizations, resulting in increased labor costs and decreased company wide productivity. New tools are required to increase productivity. This proposal is for research to produce a modeling and forecasting software system that use combination of neural net and rules based algorithms to simulate enterprise systems. Individual components of the enterprise system will be modeled with appropriate neural nets trained by constructive techniques. The enterprise system will be modeled by connecting these component neural nets with data pipe rule-based software. Such software would be able to forecast strains on the system and identify bottlenecks in the network. The completed modeling and forecasting software will become a cornerstone technology for a tool that will diagnose, troubleshoot and predict IT issues. The project will be developed in Linux with C, C++ or FORTRAN, as appropriate.This research could help in making the management of enterprise systems and networks more efficient therefore making them more affordable to teaching and service institutions.The result of this endeavor will assist enterprise systems and network managers to manage the tasks in faster and more economically manner.