Weight and Structure Determination Neural Network Aided With Double Pseudoinversion for Diagnosis of Flat Foot

Deep learning models often have complicated structures with low computational speed and the requirement of a large amount of storage space, which limits their own practical application on some devices with insufficient computing power. This paper proposes the weight and structure determination neural network aided with double pseudoinversion (WASDNN-DP) that can overcome these shortcomings. First, the model structure, theoretical bases, and the algorithms of WASDNN-DP are given. In the process of constructing the network, the weight matrix between the hidden layer and the output layer is first randomly generated. After the weight matrix between the input layer and the hidden layer is analytically determined, the weight matrix between the hidden layer and the output layer is re-determined by the pseudo-inverse method. Furthermore, in WASDNN-DP, the structure of the neural network is determined by a progressive method. Subsequently, based on two datasets collected from children aged 7-15 years by using smart insoles, the comparative experiments between WASDNN-DP and some other machine learning models are carried out, which illustrate the superiority of the proposed WASDNN-DP in the diagnosis of flat foot.