Abstract
In order to improve the channel equalization, improve the control quality, and reduce the error of the target output of the online sequential learning machine, a multi-objective model selection algorithm is proposed based on feedback compensation and adaptive equalization control. The channel equalization model of online sequential ultimate learning machine is constructed. The sensor fusion information of online sequential limit learning machine is selected adaptively by multi-objective combined control, and the multi-objective combined control is carried out by using matched filtering method. Combined with feedback compensation and adaptive equalization control method, the classification selection and equalization of network multi-objective models are realized. The simulation results show that the algorithm has good accuracy in classifying and selecting multi-objective models of online sequential LLM, the adaptive equalization performance of the channel is better, and the error of LLM control is low.
Highlights
With the development of network information technology, using network control to transmit data becomes a necessary means for people to transmit data and exchange information
Extreme learning machine (ELM) algorithm is proposed by Huang based on a single-hidden layer feedforward neural network (SLFN) algorithm
The slow learning speed of ELM algorithm is taken based on the fact that the learning ability of SLFN is only related to the number of hidden layer nodes but independent of the weight of the input layer
Summary
With the development of network information technology, using network control to transmit data becomes a necessary means for people to transmit data and exchange information. Extreme learning machine (ELM) algorithm is proposed by Huang based on a single-hidden layer feedforward neural network (SLFN) algorithm. Different from the traditional neural network algorithm based on gradient descent and error back propagation, it is necessary to iterate and determine the optimal solution of all parameters several times during the training process, which results in a large amount of computation [5]. In order to solve the above problems, this paper proposes a multi-objective model selection algorithm based on feedback compensation and adaptive equalization control. The channel equalization model of online sequential ultimate learning machine is constructed. Combined with feedback compensation and adaptive equalization control method, the classification selection and equalization of network multi-objective models are realized. The performance test is carried out through the simulation experiment, which shows the superior performance of this method in improving the ability of selecting the multi-objective model of the ultimate learning machine
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