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This study proposes a batch-learning self-organizing map with false-neighbor degree between neurons (called BL-FNSOM). False-neighbor degrees are allocated between adjacent rows and adjacent columns of BL-FNSOM. The initial values of all of the false-neighbor degrees are set to zero, however, they are increased with learning, and the false-neighbor degrees act as a burden of the distance between map...
We have investigated strategies for enhancing ensemble learning algorithms for the analysis of high-dimensional biological data. Specifically we investigated strategies to force classifiers to consider the possible interactions between features. As a result an algorithm that induces decision trees with a feature non-replacement mechanism has been devised and tested on DNA microarray and proteomic...
As linear model predictive control (MPC) becomes a standard technology, nonlinear MPC (NMPC) approach is debuting both in academia and industry. In this paper, the NMPC problem is formulated as a convex quadratic programming problem based on nonlinear model prediction and linearization. A recurrent neural network for NMPC is then applied for solving the quadratic programming problem. The proposed...
The main-flow is wandering for sedimentation in downstream channel of the Yellow River, which threatened security greatly for flood control in the lower. It is a very difficult issue to interpret the main-flow information using remote sensing image. In this paper, with the flow characteristics of direction and continuously similarity in river channel, a Dynamic Transmission Cross-Correlation (DTCC)...
In a fully complex-valued feed-forward network, the convergence of the complex-valued back-propagation learning algorithm depends on the choice of the activation function, minimization criterion, initial weights and the learning rate. The minimization criteria used in the existing learning algorithms do not approximate the phase well in complex-valued function approximation problems. This aspect is...
A solution for the load balancing problem in local clusters of heterogeneous processors is proposed within the setting of delayed artificial neural networks, optimal control and linear matrix inequalities (LMI) theory. Based on a mathematical model that includes delays and processors with different processing velocities, this model is transformed into a special case of delayed cellular neural networks...
This paper aims at developing near optimal traffic signal control for multi-intersection in city. Fuzzy control is widely used in traffic signal control. For improving fuzzy controlpsilas adaptability in fluctuate states, a controller combined with neuro-fuzzy system and adaptive dynamic programming (ADP) is designed. This controller can be used for cooperative control of multi-intersection. The adaptive...
In this paper, extreme learning machine (ELM) is introduced to predict the subcellular localization of proteins based on the frequent subsequences. It is proved that ELM is extremely fast and can provide good generalization performance. We evaluated the performance of ELM on four localization sites with frequent subsequences as the feature space. A new parameter called Comparesup was introduced to...
It is often that the learned neural networks end with different decision boundaries under the variations of training data, learning algorithms, architectures, and initial random weights. Such variations are helpful in designing neural network ensembles, but are harmful for making unstable performances, i.e., large variances among different learnings. This paper discusses how to reduce such variances...
Traditional neural network approaches for traffic flow forecasting are usually single task learning (STL) models, which do not take advantage of the information provided by related tasks. In contrast to STL, multitask learning (MTL) has the potential to improve generalization by transferring information in training signals of extra tasks. In this paper, MTL based neural networks are used for traffic...
Increasing dependence on car-based travel has led to the daily occurrence of freeway congestions around the world. In order to improve the worse and worse traffic congestion situation and solve the problems brought with it, a new kind of effective, fast, and robust method should be presented. Ramp metering has been developed as a traffic management strategy to alleviate congestion on freeways. But,...
Parameter estimation plays an important role in systems biology in helping to understand the complex behavior of signal transduction networks. The problem becomes more intense as the inherent stochasticity of the signaling mechanism involves noise components of non-Gaussian nature. A novel stochastic parameter estimation method has been developed where the aim is to obtain the optimal parameters corresponding...
In this paper, neural network based ensemble learning methods are introduced in predicting activities of COX-2 inhibitors in Chinese medicine quantitative structure-activity relationship (QSAR) research. Three different ensemble learning methods: bagging, boosting and random subspace are tested using neural networks as basic regression rules. Experiments show that all three methods, especially boosting,...
In this paper, the dynamics of weights of perceptrons are investigated based on the perceptron training algorithm. In particular, the condition that the system map is not injective is derived. Based on the derived condition, an invariant set that results to a bijective invariant map is characterized. Also, it is shown that some weights outside the invariant set will be moved to the invariant set....
Jet engine gas path fault diagnosis is not only important in modern condition-based maintenance of aircraft engines, but also a challenging classification problem. Exploring more advanced classification techniques for achieving improved classification performance for gas path fault diagnosis, therefore, has been increasingly active in recent years in PHM community. To that end, in this paper, we apply...
Conventional speaker identification and speech recognition algorithms cannot deal with noisy and multiple speaker environments. For example, IBM via Voice has low recognition rates if dictation is done in a noisy environment. In order to achieve high performance in speaker identification and speech recognition, we propose an integrated approach that takes every facet of the process into account. Here...
We present a programmable dynamic charge transfer synapse (CTS) in a single semiconductor device. The CTS comprises a metal oxide semiconductor (MOS) transistor operating in subthreshold and two MOS capacitors in proximity to the transistor. One of the capacitors is permanently biased in strong inversion where the associated density of charge in the well implements the weighting. When a presynaptic...
Semantic scene classification, robotic state recognition, and many other real-world applications involve multi-label classification with imbalanced data. In this paper, we address these problems by using an enrichment process in neural net training. The enrichment process can manage the imbalanced data and train the neural net with high classification accuracy. Experimental results on a robotic arm...
We present an analytic and geometric view of the sample mean of graphs. The theoretical framework yields efficient subgradient methods for approximating a structural mean and a simple plug-in mechanism to extend existing central clustering algorithms to graphs. Experiments in clustering protein structures show the benefits of the proposed theory.
Document representation is one of the crucial components that determine the effectiveness of text classification tasks. Traditional document representation approaches typically adopt a popular bag-of-word method as the underlying document representation. Although itpsilas a simple and efficient method, the major shortcoming of bag-of-word representation is in the independent of word feature assumption...
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