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Extreme Learning Machine (ELM) is a new type of feedforward neural network (FNN). Compared with traditional single hidden layer FNN,ELM possesses higher speed and better performance. Due to the random determination of the input weights and hidden layer biases, ELM may need more hidden neurons to achieve a reasonable accuracy. In this paper, a novel ELM learning algorithm optimized by Particle Swarm Optimization (PSO) which called PSO-ELM is proposed. PSO-ELM uses PSO algorithm to select input weights and hidden layer biases. The experimental results based on SinC benchmarking function and a large number of classification experiments from UCI standard data set show that the new algorithm can produce better generalization performance with less hidden neurons than traditional ELM algorithms and conventional feedforward neural networks. Introduction In the past decades, neural networks based on gradient descent algorithms such as back propagation (BP) neural network have been widely applied in modeling, prediction, classification, and regression applications [1-4]. ELM has got an extensive concern of many scholars, because of faster learning speed, better performance and simpler parameters adjusting compared with conventional FNNs [6,7]. However ELM algorithm has a main drawback, the random chosen input weights and hidden layer biases of the SLFN with ELM are unlikely to be optimal. This has raised the question of how to create optimal input weights, hidden neurons and bias for a given task. Particle swarm optimization (PSO) is a population-based optimization technique that emulates the social behavior of animals, such as the swarming of insects, the flocking of birds, and the schooling of fish, when searching for food in a collaborative manner. This technique was originally designed and introduced by Eberhart and Kennedy [7,8]. PSO has been widespread applied in applications such as optimal control and design [9], biomedical [10], clustering and classification [11]. To improve the generalization performance of ELM and to optimize its input weights and hidden layer bias, this paper introduces an optimized PSO-ELM that utilized the PSO algorithm to optimized the input weights and hidden layer bias of SLFN. The remainder of this article is organized as follows: Section 2 provides a brief overview of ELM. Section 3 presents a short review of the PSO algorithm. Section 4 presents the new PSO-ELM algorithm of this paper. Section 5 discusses the experimental results. Finally, Section 6 presents a brief conclusion. Extreme learning machine For N arbitrary distinct samples (xi,yi), where 1 2 , , , [ ] T n i i i im x x x x R = ... ∈ and 1 2 , , , [ ] T m i i i im y y y y R = ... ∈ , A typical SLFN with L hidden neurons and activation function f(x) can 2017 International Conference on Computing, Communications and Automation(I3CA 2017) Copyright © (2017) Francis Academic Press , UK 6

A New Extreme Learning Machine Optimized by Particle Swarm Optimization