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This paper improves stock market prediction based on genetic algorithms (GA) and wavelet neural networks (WNN) and reports significantly better accuracies compared to existing approaches to stock market prediction, including the hierarchical GA (HGA) WNN. Specifically, we added information such as trading volume as inputs and we used the Morlet wavelet function instead of Morlet-Gaussian wavelet function...
Software maintenance is assuming ever more a crucial role in the lifecycle of software due to the increase of software requirements and the high variability of software environment. Common approaches of studying software maintenance are to consider them as a static by-product of software operation and only the maintenance cost is covered. In this paper, software maintenance policies are studied with...
Many existing studies on human learning pay almost exclusive attention to how individuals learn. Unlike those studies, we examined influence of social structures on knowledge acquired by societies using computer simulations. We compared four types of social networks, namely regular, random, small world, and scale-free networks. When individual differences and the principle of homophily (i.e., people...
The emergence of Information Technology (IT) based sensing has received increasing attention and acceptance in buildings due to its noninvasive nature and its ability in delivering real-time and potentially highly personalized feedback to building energy and comfort management. This study presents results of a pilot deployment experiment on such an IT-based sensing system — Personal Office Energy...
This paper presents the design of a methodology for diagnosing sensor faults in heating, ventilation and air-conditioning (HVAC) systems, and compensating their effects on the distributed control architecture. The proposed methodology is developed in a distributed framework, considering a multi-zone HVAC system as a set of interconnected, nonlinear subsystems. For each of the interconnected subsystems,...
Use of multiple kernels in the conventional kernel algorithms is gaining much popularity as it addresses the kernel selection problem as well as improves the performance. Kernel least mean square (KLMS) has been extended to multiple kernels recently using different approaches, one of which is mixture kernel least mean square (MxKLMS). Although this method addresses the kernel selection problem, and...
In this paper, a new fully unsupervised approach to fault detection and identification is proposed. It is based on a two-stage algorithm and starts with the recursive density estimation (RDE) in the feature space. The choice of the features is important and in the real world process that we consider these are control and error related variables. The basis of the proposed approach is the fully unsupervised...
During the years 1999–2005, the environmental sustainability index (ESI) constituted a predominant tool for evaluating, ranking, and grouping countries in terms of their current and future potential to protect the environment. In this piece of research, an investigation of the calculation/prediction, ranking, and clustering capabilities of the ESI 2005 is performed using traditional as well as computational...
Although Nonnegative Matrix Factorization (NMF) has been widely known as an effective feature extraction method, which provides part-based representation and good reconstruction, there were relatively few researches using NMF for color image processing. Particularly, many studies are now using Convolutional Neural Network (CNN) in combined with Auto-Encoder (AE) or Restricted Boltzmann Machine (RBM)...
In this paper, an automatic clustering algorithm applied to self-organizing map (SOM) neurons is presented. The connections of the SOM grid are pruned according to a weighted sum of a set of measures of connection strength between adjacent neurons. The coefficients of the weighted sum are obtained through particle swarm optimization (PSO) search in the multidimensional problem space, where the fitness...
Extreme Learning Machine (ELM) is a single-hidden-layer feedforward neural network which has been applied into many real world pattern classification problems. Recently, ELMs have been built in an automatic way through evolutionary algorithms. Most works, nonetheless, do not uses all population obtained, but choose only one individual in the last generation. In an attempt to improve performance, an...
Emerging ferroelectric tunnel memristors show large OFF/ON resistance ratio (>100) and high operation speed (∼10ns), promising to be widely applied in the future synapse-like systems. In this paper we propose a neuromorphic network with ferroelectric tunnel memristor. This network is arranged with classical crossbar topology, in which each crosspoint forms a synapse consisting of a MOS transistor...
A non-invasive Brain Computer Interface (BCI) based on a Convolutional Neural Network (CNN) is presented as a novel approach for navigation in Virtual Environment (VE). The developed navigation control interface relies on Steady State Visually Evoked Potentials (SSVEP), whose features are discriminated in real time in the electroencephalographic (EEG) data by means of the CNN. The proposed approach...
Complete area coverage navigation (CAC) requires a special type of robot path planning, where the robots should visit every point of the state workspace. CAC is an essential issue for cleaning robots and many other robotic applications. Real-time complete area coverage path planning is desirable for efficient performance in many applications. In this paper, a novel vertical cell-decomposition (VCD)...
The auditory cortex in the brain does effortlessly a better job of extracting information from the acoustic world than our current generation of signal processing algorithms. The proposed architecture, Hierarchical Linear Dynamical System (HLDS), is based on Kaiman filters with hierarchically coupled state models that stabilize the input dynamics and provide a representation space. This approach extracts...
As sea level rises and coastal populations continue to grow, there is an increased demand for understanding the accurate position of the shorelines. The automatic extraction of shorelines utilizing the digital elevation models (DEMs) obtained from light detection and ranging (LiDAR), aerial images and multi-spectral images has become very promising. In this paper, we propose a new algorithm that can...
There is an extensive literature on value function approximation for approximate dynamic programming (ADP). Multilayer perceptrons (MLPs) and radial basis functions (RBFs), among others, are typical approximators for value functions in ADP. Similar approaches have been taken for policy approximation. In this paper, we propose a new Volterra series based structure for actor approximation in ADP. The...
In this work, a neural control scheme to regulate carbon monoxide (CO) and nitrogen oxides (NOx) emissions for a solid waste incinerator is proposed. Carbon monoxide emissions are avoided by oxygen regulation in the incinerator; nevertheless nitrogen oxides emissions are difficult to control because the sludge composition varies continuously. The air flow is selected to be the control input because...
In our previous works [1, 2], we proposed NEVE, a model that uses a weighted ensemble of neural network classifiers for adaptive learning, trained by means of a quantum-inspired evolutionary algorithm (QIEA). We showed that the neuro-evolutionary classifiers were able to learn the dataset and to quickly respond to any drifts on the underlying data. Now, we are particularly interested on analyzing...
Accurate forecasts of weather conditions are of the utmost importance for the management and operation of renewable energy sources with intermittent (stochastic) production. With the growing amount of intermittent energy sources, the need for precise weather predictions increases. Production of energy from renewable power sources, such as wind and solar, can be predicted using numerical weather prediction...
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