This paper presents a short introduction to the Reservoir Computing and Extreme Learning Machine main ideas and developments. While both methods make use of Neural Networks and Random Projections, Reservoir Computing allows the network to have a recurrent structure, while the Extreme Learning Machine is a Feedforward neural network only. Some state of the art techniques are briefly presented an...

This paper proposes a new structure of wavelet extreme learning machine i.e. an adaptive wavelet extreme learning machine (AW-ELM) for finger motion recognition using only two EMG channels. The adaptation mechanism is performed by adjusting the wavelet shape based on the input information. The performance of the proposed method is compared to ELM using wavelet (W-ELM0 and sigmoid (Sig-ELM) acti...

The optical domain is a promising field for the physical implementation of neural networks, due to speed and parallelism optics. Extreme learning machines (ELMs) are feed-forward networks in which only output weights trained, while internal connections randomly selected left untrained. Here we report on photonic ELM based frequency-multiplexed fiber setup. Multiplication by can be performed eit...

Extreme learning machine (ELM) is a new single hidden layer feedback neural network. The weights of the input layer and the biases of neurons in hidden layer are randomly generated; the weights of the output layer can be analytically determined. ELM has been achieved good results for a large number of classification tasks. In this paper, a new extreme learning machine called rough extreme learn...