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With the rapid development of microgrids, generator excitation control for multi-machine systems to improve the stability of power systems has become a key technical problem. This paper presents an excitation controller design for a typical two-machine system. According to the characteristics of strong nonlinearity, load disturbance and time-varying uncertainty, conventional PID control schemes cannot...
In this paper, design of a neural network for a domain-specific problem is described. The problem of concern is forecasting flood events where data is contaminated heavily by noise, training examples have different importance levels and noisy data coincides with the most important ones. To this end, two ideas are explored namely, changing the loss function and integrating a coefficient that reflects...
We present spiking neural circuits with spike-time dependent adaptive synapses capable of performing a variety of basic mathematical computations. These circuits encode and process information in the spike rates that lie between 40–140 Hz. The synapses in our circuit obey simple, local and spike-time dependent adaptation rules. We demonstrate that our circuits can perform the fundamental operations...
Deep neural networks have become the state-of-the-art approach for classification in machine learning, and Deep Belief Networks (DBNs) are one of its most successful representatives. DBNs consist of many neuron-like units, which are connected only to neurons in neighboring layers. Larger DBNs have been shown to perform better, but scaling-up poses problems for conventional CPUs, which calls for efficient...
Utility companies provide electricity to a large number of consumers. These companies need to have an accurate forecast of the next day electricity demand. Any forecast errors will result in either reliability issues or increased costs for the company. Because of the widespread roll-out of smart meters, a large amount of high resolution consumption data is now accessible which was not available in...
Understanding the relationship between fMRI BOLD and underlying neuronal activity has been crucial to connect circuit behavior to cognitive functions. In this paper, we modeled fMRI BOLD reconstructions with general linear model and balloon modeling using biophysical models of rat cerebellum granular layer and stimuli spike trains of various response times. Linear convolution of the hemodynamic response...
This paper combines an efficient reinforcement learning algorithm named Multisamples in Each Cell (MEC) with a building thermal comfort control problem. It implements the efficient exploration rule and makes high use of observed samples. A grid is utilized to partition the continuous state into cells that are used to store samples. A near-upper Q function is obtained based on the samples in each cell...
Spiking neural network (SNN) models describe key aspects of neural function in a computationally efficient manner and have been used to construct large-scale brain models. Large-scale SNNs are challenging to implement, as they demand high-bandwidth communication, a large amount of memory, and are computationally intensive. Additionally, tuning parameters of these models becomes more difficult and...
Respiratory diseases, such as pneumonia, cold, flu, and bronchitis, are still the leading causes of child mortality in the world. One solution for alleviating this problem is developing affordable respiratory-health assessment methods using computerized respiratory-sound analysis. This approach has become an active research area due to the recent developments of electronic recording devices, such...
Similarity learning ranges over an extensive field in machine learning and pattern recognition. This paper deals with similarity learning based on multiple support vector data description (SVDD). It is well known that SVDD was proposed for one-class or two-class unbalanced learning problems. Thus, we propose a multiple SVDD (MSVDD) algorithm and apply it to multi-class learning problems. A SVDD model...
In this paper, we show how metal-oxide (OxRAM) based nanoscale memory devices can be exploited to design low-power Extreme Learning Machine (ELM) architectures. In particular we fabricated HfO2 and TiO2 based OxRAM devices, and exploited their intrinsic resistance spread characteristics to realize ELM hidden layer weights and neuron biases. To validate our proposed OxRAM-ELM architecture, full-scale...
This paper presents a new strategy to build multi tree hierarchical structure SVM which can get a more efficient and accuracy classification model for multiclass problems. Base on the theory of Binary Tree SVM (BTS), we proposed an improvement algorithm which extend binary tree structure to a multi tree structure, In the multi tree hierarchical structure, similarity clustering method was proposed...
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