AIR POLLUTION – MONITORING MODELLING AND HEALTH

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248 Air Pollution <strong>–</strong> Monitoring, Modelling and Health Z i =(X i -μ i )/σ i where X i was the ith parameter, Z i was the scaled variable following a normal distribution and σ i , μ i were the standard deviation and the mean value of the ith parameter. These networks were trained in an iterative process. A single hidden sub layer architecture was followed in order to reduce the complexity of the network, and increase the computational efficiency (Haykin, 1998). Two units were chosen in the hidden layer. The schematic representation of the neural network is given in Fig. 1. Fig. 1. Multi-layer perceptron network structure. As regards the RBF network (Bishop, 1995; Haykin, 1998; Ripley, 1996; Tao, 1993; Uykan et al., 2000), the architecture that was developed included nine neurons in the hidden layer. The transfer functions (hidden layer activation functions and output function) determine the output by depicting the result of the distance function (Bors & Pitas, 2001; Iliadis, 2007). The schematic representation of the neural network with transfer functions is given in Fig. 2.
Developing Neural Networks to Investigate Relationships Between Air Quality and Quality of Life Indicators 249 Fig. 2. Radial-basis function network structure.
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AIR POLLUTION - MONITORING, MODELLI
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Contents Preface IX Chapter 1 Urban
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Preface This book provides a compre
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AIR POLLUTION – MONITORING MODELLING AND HEALTH

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