MLP-KAN: implementation of the Kolmogorov-Arnold layer in a multilayer perceptron - Galchonkov, Oleg; Baranov, Oleksii; Maslov, Oleh; Babych, Mykola; Baskov, Illia (Eastern-European Journal of Enterprise Technologies, 2025)
The object of this study is neural networks used for categorizing objects in images. The task addressed in the work is to identify options for building a multilayer perceptron architecture that apply the Kolmogorov-Arnold layer and are characterized by the best ratio of classification quality and computational effort. The paper proposes a modification to the multilayer perceptron (MLP) by replacing the first hidden layer with a Kolmogorov-Arnold layer. This allowed the use of the approximating properties of neurons and learning activation functions simultaneously. A feature of the designed MLP-KAN neural network, unlike the classical KAN network, is the use of only one activation function for each of the input elements. The training of activation functions is carried out on the basis of invariant radial basis functions, which are composed using learning weight coefficients. Such construction of the MLP-KAN neural network architecture allowed the use of typical libraries and optimizers for its training. In this case, unlike known analogs, there is no slowdown in the learning speed. Experimental studies on the handwritten digit dataset (MNIST) have shown that MLP-KAN could provide higher classification quality with less computational effort. In particular, to obtain classification quality comparable to MLP, with the appropriate parameter setting, MLP-KAN requires 3.63 times less computational effort than MLP. This makes it possible to significantly improve the efficiency of image object classification devices built on microprocessors operating under an autonomous mode as part of robotic systems