Abstract
In recent years, the dendritic neural model has been widely employed in various fields because of its simple structure and inexpensive cost. Traditional numerical optimization is ineffective for the parameter optimization problem of the dendritic neural model; it is easy to fall into local in the optimization process, resulting in poor performance of the model. This paper proposes an intelligent dendritic neural model firstly, which uses the intelligent optimization algorithm to optimize the model instead of the traditional dendritic neural model with a backpropagation algorithm. The experiment compares the performance of ten representative intelligent optimization algorithms in six classification datasets. The optimal combination of user-defined parameters for the model evaluates by using Taguchi’s method, systemically. The results show that the performance of an intelligent dendritic neural model is significantly better than a traditional dendritic neural model. The intelligent dendritic neural model has small classification errors and high accuracy, which provides an effective approach for the application of dendritic neural model in engineering classification problems. In addition, among ten intelligent optimization algorithms, an evolutionary algorithm called biogeographic optimization algorithm has excellent performance, and can quickly obtain high-quality solutions and excellent convergence speed.
Highlights
Since the dawn of the big data era, every corner of human society has accumulated a large amount of data
In 1986, with research based on multilayer neural networks, Rumelhart put forward the backpropagation algorithm (BP) for weight correction of a multilayer neural network [12]
The experimental results show that the intelligent dendritic neural model (DNM-BBO) is obviously superior to the traditional dendritic neupoor effect on some datasets (Australian Credit Approval, Diabetic Retinopathy), BBO has good robustness
Summary
Since the dawn of the big data era, every corner of human society has accumulated a large amount of data. References [27,35], respectively, proposed to use particle swarm optimization and a states of matter search algorithm as the optimization algorithm Their comparative experiment is single, and there is a lack of systematic and complete research on the application of intelligent optimization algorithm in dendritic neural models. With the continuous innovation of evolutionary computation, intelligent algorithm has a rapid development and a wide range of practical applications in various fields such as model symmetry/asymmetry, model architecture and hyper-parameters, clustering and prediction, becoming a novel method to solve traditional optimization problems in machine learning. This paper proposes an intelligent dendritic neural model firstly, which uses an intelligent optimization algorithm to optimize the model instead of the traditional backpropagation algorithm. 1. This paper proposes an intelligent dendritic neural model firstly, which uses a3nofin35telligent optimization algorithm to optimize the model instead of the traditional backpropagation algorithm.
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