Abstract
Randomized weights neural networks have fast learning speed and good generalization performance with one single hidden layer structure. Input weighs of the hidden layer are produced randomly. By employing certain activation function, outputs of the hidden layer are calculated with some randomization. Output weights are computed using pseudo inverse. Mutual information can be used to measure mutual dependence of two variables quantitatively based on the probability theory. In this paper, these hidden layer’s outputs that relate to prediction variable closely are selected with the simple mutual information based feature selection method. These hidden nodes with high mutual information values are maintained as a new hidden layer. Thus, the size of the hidden layer is reduced. The new hidden layer’s output weights are learned with the pseudo inverse method. The proposed method is compared with the original randomized algorithms using concrete compressive strength benchmark dataset.
Highlights
Machine learning (ML)-based data analysis has been a hot focuses in different disciplines
BPNN suffers from local optima, uncontrolled convergence speed and over-fitting problems
A special single-layer feed-forward (SLFN) networks-based neural networks learning algorithm, i.e., randomized weights neural networks, was proposed to overcome shortcomings that caused by the gradient-based learning algorithms [2] [3]
Summary
Machine learning (ML)-based data analysis has been a hot focuses in different disciplines. There, this randomized weights neural networks algorithm has faster learning speed, which has been successfully applied [4] [5]. We can only select some hidden layer’s outputs that relate the prediction variables more closely to calculate output weights using pseudo inverse method. Motivated by the above problems, a modified randomized weight neural networks based on MI is proposed in this paper. Pseudo inverse method is used to compute weights between these selected hidden layer’s outputs and predicted variable. Simulation based on concrete compressive strength benchmark dataset is used to validate the proposed method
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
