An architectural framework for hybrid intelligent systems: Implementation issues

Article type: Research Article

Authors: Lertpalangsunti, Narate | Chan, Christine W.^{; *}

Affiliations: Department of Computer Science/Energy Informatics Laboratory, University of Regina, Regina, Saskatchewan, Canada S4S 0A2. Tel.: +1 306 585 5225; Fax: +1 306 585 4745; E-mail: chan@cs.uregina.ca

Correspondence: [*] Corresponding author.

Abstract: This paper presents an implemented framework for intelligent system integration based on the concept of intercommunicating hybrids. The implemented toolset based on the framework is called the Intelligent Forecasters Construction Set (IFCS), which is a hybrid-programming environment that allows the developer to implement forecasters by means of neural network modules, object-oriented visual programming, knowledge-based programming and procedural programming. Neural network modules, rules, procedures and other intelligent techniques are encapsulated into blocks which can connect with each other as data flow diagrams for data processing. The flow diagrams can be organized into a hierarchy of workspaces to solve problems. The system was implemented on the real-time expert system shell G211G2, GDA and NeurOn-Line (NOL) are trademarks of Gensym Corp., USA., with G2 Diagnostic Assistant (GDA1) and NeurOn-Line1 (NOL) modules. The modularity of IFCS allows subsequent addition of other modules of intelligent techniques. The IFCS was used for developing forecasters of daily electricity demand and water demand at the City of Regina based on the idea of homogeneous multi-module system. In both cases, the data sets were separated into subclasses and each of them was modeled with a neural network module. The two problem domains were also modeled using a linear regression (LR) and a case based reasoning (CBR) program. The benefits of a multi-module neural network approach are discussed and some experimental results from the applications are presented.

Keywords: data analysis, neural networks

DOI: 10.3233/IDA-2000-43-414

Journal: Intelligent Data Analysis, vol. 4, no. 3-4, pp. 375-393, 2000