Electricity inspection is important to support sustainable development and core the marketing of electric power. In addition, it contributes effective management power companies their financial performance. Continuous improvement in penetration rate new energy generation can improve environmental standards promote development, but creates challenges for electricity inspection. Traditional metho...

Extreme Learning Machine (ELM) is a neural network architecture in which hidden layer weights are randomly chosen and output layer weights determined analytically. We interpret ELM as an approximation to a network with infinite number of hidden units. The operation of the infinite network is captured by neural network kernel (NNK). We compare ELM and NNK both as part of a kernel method and in n...

In multimodal biometric system, the effective fusion method is necessary for combining information from various modality systems. In this study a new approach to overcome the limitations by using multiple pieces of evidence of the same identity: iris and fingerprint, by combining ELM and Genetic Algorithm. According to ELM theory: “The hidden node / neuron parameters are not only independent of...

This paper is devoted to yet unpublished electrode-less methods (ELM) of cell layers impedance measurement based on transformer principle. The main advantage of ELM is elimination uncertainties caused by interface between electrodes and measured electrolyte. The method of avoiding distortion caused by non-ideal transformer transfer function ("deconvolution") and errors caused by residual voltag...

Error Minimized Extreme Learning Machine (EM-ELM) proposed by Feng et al. [1] can automatically determine the number of hidden nodes in generalized Single-hidden Layer Feedforward Networks (SLFNs). We recently found that some of the hidden nodes that are added into the network may play a very minor role in the network output, which increases the network complexity. Hence, this paper proposes an...

We describe a novel maximum likelihood nonlinear feature bias compensation method for Gaussian mixture model–hidden Markov model (GMM–HMM) adaptation. Our approach exploits a single-hiddenlayer neural network (SHLNN) that, similar to the extreme learning machine (ELM), uses randomly generated lower-layer weights and linear output units. Different from the conventional ELM, however, our approach...

The success of myoelectric pattern recognition (M-PR) mostly relies on the features extracted and classifier employed. This paper proposes and evaluates a fast classifier, extreme learning machine (ELM), to classify individual and combined finger movements on amputees and non-amputees. ELM is a single hidden layer feed-forward network (SLFN) that avoids iterative learning by determining input w...

In this paper,we propose a new approach for writer identification of Chinese handwritten.In our method, we deal with writer identification of Chinese handwritten using Chinese character structure features(CSF) and extreme learning machine(ELM).To extract the features embedded in Chinese handwriting characters, special structures have been explored according to the trait of Chinese handwriting c...

Epiluminescence microscopy (ELM) is a non invasive technique used to enhance visualization of microscopic structures of pigmented lesions for the early detection of melanoma. The 7 point check list is a diagnostic method that requires the identification of only seven dermoscopic criteria, defining the image through the use of algorithms. This paper describes an experimental automated diagnosis ...