Innovation in artificial neural network learning: Learn-On-Demand methodology

The artificial neural networks represent the state of the art tool for forecasting and prediction. However, the technique relies heavily on the availability of adequate data for its training. There have been many attempts to overcome the problems associated with the acquisition of learning data. These include the use of simulation techniques, which prepare the data for pre-processing prior to learning. Nevertheless, these methods tend to undermine the specific nature of the application that is reflected in its data. Furthermore, it is evident that, in certain circumstances, the current learning methods, grouped under on-line and off-line, do not provide an effective learning solution and their advantages are mutually exclusive. With these problems in mind, this research proposes a method for rectifying these shortcomings. The solution focuses on the learning processes rather than data. The work offers a new learning mechanism, namely the “Learn-On-Demand” (LOD) methodology, which enables the ANN to learn where the lack of knowledge is evident. The proposed LOD methodology integrates into ANN's learning process. Having produced the algorithm for its implementation, the paper then produces the mathematical representation of the Learn-On-Demand methodology by integrating the new algorithm into existing methodologies. The need for this solution emerged out of a research in the field of construction, where Structured Systems Analysis and Design was sued as a platform for integrating a hybrid of AI techniques in order to develop an enhanced method of client briefing.

Research Highlights
► The research identified an area where ANN learning methods is improved to accommodate new situations.
► The paper highlights the potential use of AI to deal with intricate issues relating to construction client briefing.
► The proposed enhanced learning mechanism combines the features of the combines the features of the two conventional methods.
► The proposed method facilitates a supervised on-line learning without the need for complete retraining.
► The learning of new situations is appended to the existing network rather than carrying out a complete relearning.