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Spikes are an important part of information transmission between neurons in the biological brain. Biological evidence shows that information is carried in the timing of individual action potentials, rather than only the firing rate. Spiking neural networks are devised to capture more biological characteristics of the brain to construct more powerful intelligent systems. In this paper, we extend our...
A convolutional neural network (CNN) is implemented on a field-programmable gate array (FPGA) and used for recognizing objects in real-time video streams. In this system, an image pyramid is constructed by successively down-scaling the input video stream. Image blocks are extracted from the image pyramid and classified by the CNN core. The detected parts are then marked on the output video frames...
Deterministic behavior can be modeled conveniently in the framework of finite automata. We present a recurrent neural network model based on biologically plausible circuit motifs that can learn deterministic transition models from given input sequences. Furthermore, we introduce simple structural constraints on the connectivity that are inspired by biology. Simulation results show that this leads...
In this paper, a novel predictive event-triggered control method based on heuristic dynamic programming (HDP) algorithm is developed for nonlinear continuous-time systems. A model network is used to estimate the system state vector, so that the event-triggered instant is available to predict one step ahead of time. Furthermore, an actor-critic structure is used to approximate the optimal event-triggered...
We propose a bootstrap-based iterative method for generating classifier ensembles called Iterative Classifier Selection Bagging (ICS-Bagging). Each iteration of ICS-Bagging has two phases: i) bootstrap sampling to generate a pool of classifiers; and, ii) selection of the best classifier of the pool using a fitness function based on the ensemble accuracy and diversity. The selected classifier is added...
Though the Extreme Learning Machine (ELM) has become quite popular in recent years, there are no performance guarantees; the resultant networks also tend to be densely connected. The complexity of a learning machine may be measured by the Vapnik-Chervonenkis (VC) dimension, and a small VC dimension leads to good generalization and lower test set errors. The Minimal Complexity Machine (MCM), that has...
Nowadays this is very popular to use deep architectures in machine learning. Deep Belief Networks (DBNs) have deep architectures to create a powerful generative model using training data. Deep Belief Networks can be used in classification and feature learning. A DBN can be learnt unsupervised and then the learnt features are suitable for a simple classifier (like a linear classifier) with a few labeled...
Collaborative filtering is used to recommend items to a user without requiring a knowledge of the item itself and tends to outperform other techniques. However, pure collaborative filtering technique suffer from the cold-start problem, which occurs when an item has not yet been rated or a user has not rated any items. Incorporating additional information, such as item or user descriptions, into collaborative...
The investigation of protein functionality often relies on the knowledge of crystal 3-D structure. This structure is not always known or easily unravelled, which is the case of eukaryotic cell membrane proteins such as G Protein-Coupled Receptors (GPCRs) and specially of those of class C, which are the target of the current study. In the absence of information about tertiary or quaternary structures,...
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...
Use of multiple scripts for information communication through various media is quite common in a multilingual country. Optical character recognition of such document images or videos assists in indexing them for effective information retrieval. Hence, script identification from multi-lingual documents/images is a necessary step for selecting the appropriate OCR, due the absence of a single OCR system...
This paper presents a two stage learning algorithm for a Growing-Pruning Spiking Neural Network (GPSNN) for pattern classification problems. The GPSNN uses three layered network architecture with input layer employing a modified population coding and, leaky integrate-and-fire spiking neurons in the hidden and output layers. The class label for a sample is determined according to the output neuron...
Global darknet monitoring provides an effective way to observe cyber-attacks that are significantly threatening network security and management. In this paper, we present a study on characterization of cyberattacks in the big stream data collected in a large scale distributed darknet using association rule learning. The experiment shows that association rule learning in the darknet stream data can...
The concept of nonnegative matrix factorization is a recent machine learning technique that is used to decompose large data matrices imposing the non-negativity constraints on the factors. This technique is now used in many data mining applications and thus remains a topic of ongoing interest. In this paper we are particularly interested in the Multilayer NMF - a model that can be seen as a pretraining...
In this paper, the effects of regularization on the generalization capabilities of a neural network model are analyzed. We compare the performance of Levenberg-Marquardt and Bayesian Regularization algorithms with and without post-training regularization. We show that although Bayesian Regularization performs slightly better than Levenberg-Marquardt, the model trained using Levenberg-Marquardt holds...
Convolutional Neural Networks (CNNs) have become forceful models in feature learning and image classification. They achieve translation invariance by spatial convolution and pooling mechanisms, while their ability in scale invariance is limited. To tackle the problem of scale variation in image classification, this work proposed a multi-scale CNN model with depth-decreasing multi-column structure...
This paper presents a methodology to implement large Neural Networks based classifiers in low-cost FPGAs. The idea is to divide the large Neural Network into several smaller networks which can easily be implemented in small devices. Then, a Multiple Classifier Ensemble is used to joint the results of each small network and thus provide the output of the system. To validate the proposal a classification...
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...
Extreme learning machine (ELM) is one of suitable base-classifiers for ensemble learning systems because of its fast learning speed, good generalization performance and simple setting. For the ensemble learning, how to select the base classifiers is a key issue which influences the performance of the ensemble system dramatically. To obtain a compact ensemble system with improved generalization performance,...
Acoustic echo cancellers are used in teleconferencing systems in order to reduce undesired echoes due to coupling between microphones and loudspeakers. Stereophonic systems provide more realistic experience than single-channel systems, since listeners have spatial information that helps to identify the speaker position. Assuming this scenario, a suitable choice for the system parameters becomes essential...
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