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This paper presents a knee torque estimation in non-pathological gait cycle at stance phase. Comparative modelling by using dynamics model and neural network model is discussed. Dynamics modelling is constructed by using simple two degree of freedom dynamics with Newtonian calculation approach and more complex four degree of freedom dynamics with Lagrangian calculation approach. Neural network based...
The detection and elimination principle of redundant elements in the mathematical model is proposed in this paper. The efficiency of the proposed approach has been analyzed based on the neural network model of economic system and differential equations model. Results prove the 16 times increasing in the model accuracy.
Students academic performance is the reflection of both academic background and family support. This performance record is critical for the educational institution because they can learn from this to improve their quality. Educational data mining helps to analyze these data and extract information from it. We can determine the status of learners academic performance. For achieving this we can use...
Reference evapotranspiration (ETo) is an important factor in the water-saving agriculture and in the soil-plant-atmosphere continuum. Many machine learning methods have been introduced into predicting ETo. In order to improve the accuracy of radiation-based ETo models, this paper presents an ETo model called RFR based on random forest (RF). By taking the results of FAO Penman-Monteith (FAOPM) model...
On-Line Learning Behavior depend on learning subject self-control learning, collaborative learning, and obtaining of support and help. Based on learning subject, the On-Line learning behavior need the real-time monitoring and the effective instruction to through the evaluation in the learning process. The BP algorithm model of evaluating E-Learning behavior selects the learning behavior which affects...
This paper presents a modeling technique of sequential batch reactor (SBR) for aerobic granular sludge (AGS) using artificial neural network (ANN). A SBR fed with synthetic wastewater was operated at high temperature of 50˚C to study the formation of AGS for simultaneous organics and nutrients removal in 60 days. The feed forward neural network (FFNN) was used to model the nutrients removal process...
This paper presents a neural network based approach to the identification and control of an experimental natural circulation loop. The aim of the model is to predict the dynamical evolution of the oscillations, characterizing the system dynamics in some operating conditions and that can cause dangerous flow reversal. The identification of the system was the first step towards the design of an appropriate...
Sequential Monte Carlo methods (Particle Filters) have been successfully applied to the online training of neural networks. However the generic Particle Filter requires the model noise to be known prior to training. Furthermore, the random walk assumption with which the network weights are modeled by may be problematic as a result of the insufficient knowledge of the model noise. In this paper, the...
This paper introduces a theoretical new approach for training the adaptive-network-based fuzzy inference system (ANFIS) using Tree Physiology Optimization (TPO). The TPO is a heuristic method based on tree physiology. The method will be applied to nonlinear dynamic system.
A Bond Graph model is built for the steering system of automatic vehicle and a set of model equations are derived for further analysis purpose. For identifying several uncertain parameters, an integrative approach that combine least square method with Bp Neural Network algorithm (NN) is proposed, based on features of NN algorithm, two key improvements are bring into the training method of Bp NN: taking...
In this paper, we introduce how to predict the parachute deploy for landing at the desired point. The UAV-parachute system is required 9-DOF dynamic modeling, so we build up the equations of motion for this system. And then the input and the output data sets are trained to compose the neural network. The input data sets are the flight conditions such as the deploy position, UAV's velocity, and wind...
By using neural networks, Beijing's water supplied and consumed is forecasted, and connection number and total partial connection number of the set pair analysis (SPA) are obtained. Principal factors and development trend are sequenced based on absolute relative error between sample value xi and predominant value yi so as to set up a mathematics model of forecasting Beijing's water supplied and consumed...
Cognitive Radio (CR) can access the spectrum temporarily to solve the problem of the near spectrum crunch. The previous transmissions' events are one of the main motivations for the CR actions and its learning procedures. Therefore, self aware CR devices may cause a considerable interference when they transmit for the first time with no practical knowledge. This paper proposes a solution for cognitive...
This paper proposed the inverse model control strategy of the inverter in the field of active power filter. Since the inverse model of the inverter can not be easily as well as accurately obtained, BP neural network is introduced to approach the inverse model. After the training of the neural network, we can connect it with the inverter to get a nearly ideal linear system. Various simulations are...
Design and development of unmanned aerial vehicles has attracted increased interest in the recent past. Rotorcraft UAVs, in particular have more challenges than its fixed wing counterparts. More research and experiments have been conducted to study the stability and control of RUAVs. A model-based control system design is particularly of our interest since it avoids a tedious trial and error process...
The identification process of the classical Preisach model which is based on a neural network approach is presented. The fundamental idea of this approach is to identify Preisach function by training a neural network with a set of loops whose identification function is already known. The suggested identification approach has been numerically implemented and carried out for a fast tool servo system...
This paper studies solutions for forecasting option prices in a volatile financial market. It reviews a mathematical model based on traditional Black-Scholes parametric solution. Then, uses neural networks and compares the results with the conventional method. Twenty year data from S&P 500 index call option prices was used in this study. Initially simple neural network was implemented. The prediction...
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