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The self-organizing map (SOM) neural network has been used widely in multiple models approximation (MMA). However, the clustering property of SOM may not be fit for MMA. This paper introduces the idea of active learning into the training of SOM, especially for MMA. The neural network selects actively the training samples according to the approximation error of local models. As a result, the distribution...
In this paper, a cellular automata (CA) model is extended to simulate the circular movements of Muslim pilgrims performing the Tawaf ritual within the Masjid Al-Haram facility in Makkah. There is scant literature on the implementation of CA in modeling circular motions. Moreover, most of the published studies do not take into account the pedestrian's ability to select the exit route in their models...
This paper uses the global optimization of genetic algorithm to construct a genetic neural network model (GANN) forecasting listed company financial crisis. The model optimizes input variables of neural network model forecasting financial crisis. Forecasting of financial distress of listed companies in Shanghai and Shenzhen A share markets indicates that this model bears a better ability to predict...
Intrusion detection system (IDS) is a technology forwarded for guaranteeing the computer system security and thus finding and reporting unauthorized or abnormal phenomenon. Neural network possesses the abilities including self-adaptive, self-organizing and self-learning. Utilizing the capacities such as recognition, classification and induction can make the IDS adaptable to the dynamic changes characteristics...
The Back Propagation Neural Network(BPNN) has been used widely in objects recognition, but in fact, the BPNN can easily be trapped into a local minimum and has slow convergence. Moreover, the number of neural cells for hidden layer in the BPNN is hard to determine. For this reason, this paper proposes a novel method to improve the performance from the structure and the algorithm. The improved BP algorithm...
This paper focuses on an intelligent decision-making model of surface warship modeling in Computer Generated Forces (CGF) System. Different kinds of AI technologies were used to found the decision-making model, enable the surface warship CGF system to select battle region, layout path, identify targets, estimate threat and control firing. The model was validated through simulating with other CGF systems...
Input variables selection plays a critical role in data-driven modelling, especially for complex systems with high dimensionality between the input/output space. In this paper, a new artificial neural network based forward input selection scheme is proposed. The objective of the proposed scheme is to select the smallest number of important variables as model inputs, which will then be used for neural-fuzzy...
Through in-depth study on the existing technologies about intrusion detection system, to accelerate the detection speed and improve the accuracy, this paper presents a new intrusion detection model based on neural networks. This model uses neural networks to detect, transforms the pattern recognition into numerical calculation, thereby speeding up the detection rate, while combining with expert system...
The accurate simulation of anatomical joint models is becoming increasingly important for both realistic animation and diagnostic medical applications. Recent models have exploited unit quaternions to eliminate singularities when modelling orientations between limbs at a joint. This has led to the development of quaternion based joint constraint validation and correction methods. In this paper a novel...
This paper proposes a back propagation neural network based real-time humanoid self-collision detection method which eliminates the repetition of detection computation for same and similar motion sets. The proposed system is able to reduce self-collision detection computation time significantly, because of the pattern recognition capability of the neural network. However, the accuracy of back propagation...
Different diagnosis models, including multiplayer perceptron (MLP), radial basis function (RBF) and two types of support vector machines (SVMs), were designed, analyzed and compared based on the fault diagnosis of an analogue circuit instance. The experimental results show SVM model is of higher classification rate than MLP and RBF models, while MLP model has better ability to deal with uncertain...
In this paper, we present a model based on the Neural Network (NN) for classifying Arabic texts. We propose the use of Singular Value Decomposition (SVD) as a preprocessor of NN with the aim of further reducing data in terms of both size and dimensionality. Indeed, the use of SVD makes data more amenable to classification and the convergence training process faster. Specifically, the effectiveness...
Wireless Sensor Networks (WSN) is an emerging technology that is developed with a large number of useful applications. On the other hand, Artificial Neural Networks (ANN) have found many successful applications in nonlinear system and control, digital communication, pattern recognition, pattern classification, etc. There are many similarities between WSN and ANN. For example, the sensor node itself...
The prediction of fill levels in stormwater tanks is an important practical problem in water resource management. In this study state-of-the-art CI methods, i.e., Neural Networks (NN) and Genetic Programming (GP), are compared with respect to their applicability to this problem. The performance of both methods crucially depends on their parametrization. We compare different parameter tuning approaches,...
In recent years the the potential and programmability of Graphics Processing Units (GPU) has raised a note-worthy interest in the research community for applications that demand high-computational power. In particular, in financial applications containing thousands of high-dimensional samples, machine learning techniques such as neural networks are often used. One of their main limitations is that...
In order to improve the accuracy of precipitation forecasting with the linear regression of traditional statistical model and the nonlinear regression of Neural Network (NN) model, especially in torrential rain, a novel Bayesian Additive Regression Trees (BART) ensemble model is proposed in this paper. Firstly, three different linear regression model are used to extract the linear characteristic of...
Accurate forecasting of rainfall has been one of the most important issues in hydrological research. Due to rainfall forecasting involves a rather complex nonlinear data pattern; there are lots of novel forecasting approaches to improve the forecasting accuracy. This paper proposes a Projection Pursuit Regression and Neural Networks (PPR--NNs) model for forecasting monthly rainfall in summer. First...
Packet loss limits the speech quality of voice over IP and the effect is described with the packet-loss dependent effective equipment impairment factor in E-model. The existing model of the factor is associated with the speech codec and limited to the network with low loss burst Ratio. With the experiments, this paper tries to find the variables in the relation with the speech quality over packet-loss...
The paper proposes a novel neuron model termed as Generalized Power Mean Neuron model (GPMN). The paper focuses on illustrating the computational power and the generalization capability of this model. In this model, the aggregation function is based on generalized power mean of the inputs. The performance of the neural network using GPMN model is compared with traditional feed-forward neural network...
We consider the multi-class classification problem, based on vector observation sequences, where the conditional (given class observations) probability distributions for each class as well as the unconditional probability distribution of the observations are unknown. We develop a novel formulation that combines training with the quality of classification that can be obtained using the 'learned' (via...
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