Abstract
Extreme learning machine (ELM), as a new simple feedforward neural network learning algorithm, has been extensively used in practical applications because of its good generalization performance and fast learning speed. However, the standard ELM requires more hidden nodes in the application due to the random assignment of hidden layer parameters, which in turn has disadvantages such as poorly hidden layer sparsity, low adjustment ability, and complex network structure. In this paper, we propose a hybrid ELM algorithm based on the bat and cuckoo search algorithm to optimize the input weight and threshold of the ELM algorithm. We test the numerical experimental performance of function approximation and classification problems under a few benchmark datasets; simulation results show that the proposed algorithm can obtain significantly better prediction accuracy compared to similar algorithms.
Highlights
In recent years, artificial intelligence algorithms have drawn extensive attention from scientific research
The hidden layer parameters of ELM are selected appropriately to solve the problem that the hidden layer parameters need to be optimized due to randomness. erefore, this paper considers the use of the BACS algorithm to optimize ELM so as to propose a hybrid algorithm of Extreme Learning Machine based on the bat cuckoo algorithm (BACS − ELM)
In order to verify the performance of the proposed algorithm, a function fitting and several classification problems are tested and the validity of BACS − ELM is tested by comparing it with the ELM, Bat algorithm (BA) − ELM, and cuckoo search algorithm (CS) − ELM algorithms
Summary
Artificial intelligence algorithms have drawn extensive attention from scientific research. E basic thought of the BACS − ELM algorithm is to use the BACS algorithm to train the input weight and threshold value randomly generated by ELM to find the optimal parameter and determine the output weights by using MP generalized inverse so as to improve the convergence speed and stability of the network model. (1) Based on the idea of a group intelligence optimization algorithm, this paper introduces how to train ELM by BACS hybrid algorithm By using this method, the input weights and thresholds of the ELM network can be reasonably optimized to solve the randomness problem of hidden layer parameters so that the network parameters can reach the optimum.
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