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In recent years, neuromorphic hardware systems have significantly grown in size. With more and more neurons and synapses integrated in such systems, the neural connectivity and its configurability have become crucial design constraints. To tackle this problem, we introduce a generic extended graph description of connection topologies that allows a systematical analysis of connectivity in both neuromorphic...
The aim of the work is verifying the possibility of extrapolating information on demand trends, for a company specialized in the production of aluminium tins, using the data collected in previous periods. This study is mainly divided into three stages: (1) data pre-processing (data collection) stage, (2) adaptive network evaluating stage and (3) forecast and recall stage. At the stage of data collection,...
Using a dynamical model of happiness in humans, proposed in, we develop a model of happiness for a network of people using dynamical elements with interconnecting coupling factors. The network model of happiness is derived from a leader-followers network model proposed by Wang and Slotine. Such networks with interconnected dynamical elements can be found in sensor networks, electro-mechanical and...
Reducing power consumption has become a priority in microprocessor design as more devices become mobile and as the density and speed of components lead to power dissipation issues. Power allocation strategies for individual components within a chip are being researched to determine optimal configurations to balance power and performance. Modelling and estimation tools are necessary in order to understand...
Most neural networks have a basic competitive learning rule on top of a more involved processing algorithm. This work highlights three basic learning rules - winner-take-all (WTA), spike timing dependent plasticity (STDP), and inhibition of return (IOR). It also gives a design example implementing WTA combined with STDP in a position detector. A CMOS and an MMOST (Memristor-MOS Technology) design...
Gray-box neural models mix differential equations, which act as white boxes, and neural networks, used as black boxes, to complete the phenomenological model. These models have been used in different researches proving their efficacy. The aim of this work is to show the training of the gray-box model through indirect back propagation and Levenberg-Marquardt. The gray-box neural model was tested in...
Olfactory stimuli are represented in a highdimensional space by neural networks of the olfactory system. A great deal of research in olfaction has focused on this representation within the first processing stage, the olfactory bulb (vertebrates) or antennal lobe (insects) glomeruli. In particular the mapping of chemical stimuli onto olfactory glomeruli and the relation of this mapping to perceptual...
The prediction for dissolved oxygen (DO) in aquaculture ponds is a problem of multi-variables, nonlinearity and long-time lag. Neural networks (NNs) have become one of ideal tools in modeling nonlinear relationship between inputs and outputs. In this work, GA-LM, a neural network model combining Levenberg-Marquardt(LM) algorithm and Genetic Algorithm (GA) was developed for predicting DO in an aquaculture...
In this paper, we studied the two most commonly used artificial intelligence methods (Multilayer Perceptron and Radial Basis Function network) to build the credit scoring model of applications, and analyzed the most important restraining factors of the applications of neural network which is the exponential increase in the variables bringing the model over-complex. On this basis, the author combines...
Data mining (DM) is the extraction of hidden predictive information from large databases that has becoming a powerful new technology with great potential to help companies to focus on the most important information in their data warehouses. A predictive model makes a prediction about values of data using known results found from historical data where the best possible outcome based on the previous...
A brain-computer interface (BCI) creates a direct communication pathway between the brain and an external device, and can thereby restore function in people with severe motor disabilities. A core component in a BCI system is the decoding algorithm that translates brain signals into action commands of an output device. Most of current decoding algorithms are based on linear models (e.g., derived using...
This paper presents a neural-network-based active learning procedure for computer network intrusion detection. Applying data mining and machine learning techniques to network intrusion detection often faces the problem of very large training dataset size. For example, the training dataset commonly used for the DARPA KDD-1999 offline intrusion detection project contained approximately five hundred...
Hypoglycemia is dangerous for Type 1 diabetes mellitus (T1DM) patients. Based on the physiological parameters, we have developed a classification unit with hybridizing the approaches of neural networks and genetic algorithm to identify the presences of hypoglycemic episodes for TIDM patients. The proposed classification unit is built and is validated by using the real T1DM patients' data sets collected...
Large-scale neural hardware systems are trending increasingly towards the “neuromimetic” architecture: a general-purpose platform that specialises the hardware for neural networks but allows flexibility in model choice. Since the model is not hard-wired into the chip, exploration of different neural and synaptic models is not merely possible but provides a rich field for research: the possibility...
As perceived by consumers, the value of an eco-product can be enhanced by its product form in addition to physical product attributes. This paper develops a neural network (NN) and multiattribute decision making (MADM) approach for determining the design combination of product form elements that match a given eco-product value (EPV) and product image. A morphological analysis is used to extract form...
A new model is introduced in this paper to construct the input-output relation in the prediction and control problem of non-analytic systems. The historical input-output data of general system is de-noised with wavelet transformation and SVM, and the input-output variables which can reflect the features of the system are determined with correlation analysis and sensitivity analysis. With the historical...
This paper presents a neural network (NN) approach for determining the best design combination of product form elements that match a given product value represented by eco-product value (EpV) attributes. Twenty-seven representative office chairs are derived from 100 collected as the experimental samples by using multidimensional scaling and cluster analysis. Moreover, a morphological analysis is applied...
Based on the measured data of hillslope simulated rainfall experiment in the Loess Plateau of China, the method of back-propagation neural networks optimized by genetic algorithms was used to establish the hillslope runoff and infiltration model. The rainfall intensity, rainfall duration, initial soil water content and slope were selected as the model inputs, the runoff volume and infiltration volume...
Tourism environment is the base of the development of tourism industry. The evaluation on the environment quality of tourism destination can maintain and promote the quality of the eco-environment of the tourism destination. In this paper, the evaluation system of eco-agriculture tourism is set up and Neural Networks Method is used for the environmental quality assessment of eco-agriculture tourism...
The accurate and reliable Trip-generation Forecasting Model is the most basic and important part of the traffic forecasting model. This paper focuses on combining the neural network which has a strong fitting capability and genetic algorithm which has an excellent Global search capability with trip-generation forecasting model in order to achieve the purpose of improving the accuracy of prediction...
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