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Recently, the use of data-driven models is becoming increasingly impactful but has proven to offer best prediction with less knowledge of the geological, hydrological, and physical process behaviour and criteria. A Group Data Handling Model (GMDH) is one of the sub-model common neural network data driven. It was first developed for complex systems with a modelling and recognition algorithm. GMDH is known as a self-organizing heuristic modelling approach. For solving modelling issues involving multiple inputs to single output data, it is very successful. While the GMDH model has been implemented in many modelling fields, some modifications in terms of parameter design have been given little attention. In other respects, Dr. Genichi Taguchi suggested that the Taguchi method for improving the process or product design with the help of significant parameter levels that influence the delivery of the product. In this paper, we evaluated the behaviour of GMDH model based on numbers of neuron per layer, hidden layer, alpha, and train ratio parameters using Taguchi method. Cocomo and Kemerer datasets are used to test our hypothesized scenarios. The result shows that number of neurons, layer and train ratio are the important parameters that affects the performance of the GMDH model.

Parameter Design for Group Method Data Handling (GMDH) using Taguchi in Software Effort Estimation