In this paper, we bring forward a bp algorithm condition modeling and an attitude modeling based on PUMA robot vacuum path planning. Considering the limitation of the traditional particle swarm optimization(PSO) in se...
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ISBN:
(纸本)9781479984640
In this paper, we bring forward a bp algorithm condition modeling and an attitude modeling based on PUMA robot vacuum path planning. Considering the limitation of the traditional particle swarm optimization(PSO) in searching space and easily running into local optimal points. We introduced the re-initialization mechanism based on the global information feedback to modified particle swarm optimization (MPSO) algorithm in this paper. To combine the MPSO algorithm with bp network algorithm before applying it in Vacuum Path Planning of PUMA Robot. Then the mixed bp-MPSO optimal algorithm can not only avoids the difficulty in solving the inverse motion equations, but also ensures that the optimal solution, which can obtains the global optimal aim instead of falling into local extremer. The results of simulation experiment show that this algorithm can well enhance the efficiency of vacuum path planning.
Magnetic shape memory alloy actuator have hysteresis characteristics, in order to describe the hysteresis nonlinearity of magnetic shape memory alloy actuator and study the methods of eliminating the hysteresis nonlin...
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ISBN:
(纸本)9781509009107
Magnetic shape memory alloy actuator have hysteresis characteristics, in order to describe the hysteresis nonlinearity of magnetic shape memory alloy actuator and study the methods of eliminating the hysteresis nonlinearity of magnetic shape memory alloy actuator, the Krasnosel'skii-Pokrovskii(KP) model is firstly established in this paper. Secondly, bp neural network and adaptive linear neural network are applied to identify the density functions of the KP model respectively. Finally, it is obtained that the modeling accuracy of the KP model is 0.37% by bp algorithm and the modeling accuracy of the KP model based on adaptive linear neural network is 0.19% through the simulation results, which prove that the KP model can characterize the hysteresis nonlinearity of magnetic shape memory alloy actuator.
A model based on intuition fuzzy petri nets (IFPN) aiming at the dynamic and uncertain problem of tactical intention recognition is proposed and the parameters are optimized. Firstly, the author describes the situatio...
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ISBN:
(纸本)9781614996194;9781614996187
A model based on intuition fuzzy petri nets (IFPN) aiming at the dynamic and uncertain problem of tactical intention recognition is proposed and the parameters are optimized. Firstly, the author describes the situation characteristics and intention patterns of Intuitionistic Fuzzy Sets, constructs a intuitionistic fuzzy rules-table and extracts logic relation and decision-making rules;accordingly, an algorithm of rules extraction is given, and the IFPN model of intention recognition is formed. In order to improve rational precision, the error back propagation (bp) algorithm is introduced to the parameters-optimized process of IFPN. Finally, the feasibility and validity of the proposed algorithms are presented by an instance.
A fuzzy confusion matrix based cursive handwritten text categorization has been implemented. Printed text is obtained from handwritten text through Modified Optimal Clustering algorithm (MOCA). Optimal Clustering Algo...
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ISBN:
(纸本)9781509000357
A fuzzy confusion matrix based cursive handwritten text categorization has been implemented. Printed text is obtained from handwritten text through Modified Optimal Clustering algorithm (MOCA). Optimal Clustering algorithm (OCA) groups texts into different subject categories. Learning is conducted to extract the attributes along with corresponding weights for each subjects. Fuzzy confusion matrix has been used to measure several performance metrics with Holdout method. These are satisfactory. Over and above the text learning and recognition time is very less making the system efficient also.
For many decades, artificial neural network (ANN) proves successful results in thousands of problems in many disciplines. Back-propagation (bp) is one of the candidate algorithms to train ANN. Due to the way of bp to ...
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ISBN:
(纸本)9781509028634
For many decades, artificial neural network (ANN) proves successful results in thousands of problems in many disciplines. Back-propagation (bp) is one of the candidate algorithms to train ANN. Due to the way of bp to find the solution for the underlying problem, there is an important drawback of it, namely the stuck in local minima rather than the global one. Recent studies introduce meta-heuristic techniques to train ANN. The current work proposes a framework in which grey wolf optimizer (GWO) provides the initial solution to a bp ANN. Five datasets are used to benchmark GWO bp performance with other competitors. The first competitor is an optimized bp ANN based on genetic algorithm. The second is a bp ANN powered by particle swarm optimizer. The third is the bp algorithm itself and lastly a feedforward ANN enhanced by GWO. The carried experiments show that GWObp outperforms the compared algorithms.
In this paper an improved algorithm of bp neural network - Levenberg-Marquardt (LM) algorithm is introduced, and the simulation predictions of oilfield cementing quality is done by using this method. Finally, a practi...
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ISBN:
(纸本)9783037850756
In this paper an improved algorithm of bp neural network - Levenberg-Marquardt (LM) algorithm is introduced, and the simulation predictions of oilfield cementing quality is done by using this method. Finally, a practical example verified the feasibility of the presented method.
This paper takes a kind of ceramic glaze as an example, and builds an improved bp neural network model for optimizing the formulation on ceramic glaze. The improved bp neural network adopts Levenberg- Marquardt algori...
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ISBN:
(纸本)9781612848372
This paper takes a kind of ceramic glaze as an example, and builds an improved bp neural network model for optimizing the formulation on ceramic glaze. The improved bp neural network adopts Levenberg- Marquardt algorithms. The paper reviews how to build the ceramic formulation optimization model based on bp artificial neural network, including the establishment of neural network, the training, and the inspection of results. Meanwhile, the software Matlab7 has a neural network toolbox. The bp network model of ceramic formulation optimization is simulated by Matlab7. The model has achieved the good effect in the prediction precision and the algorithm convergence speed respects. So, application of improved bp algorithm has an important significance on ceramic formulation optimization research.
This paper describes the digital character recognition process and steps. Using artificial neural networks with momentum term and adaptive learning rate back-propagation algorithm to train and identify the ideal signa...
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ISBN:
(纸本)9783642233203
This paper describes the digital character recognition process and steps. Using artificial neural networks with momentum term and adaptive learning rate back-propagation algorithm to train and identify the ideal signal and noise signal containing the number of characters. By comparing the results to obtain that using the same network with the ideal signal and noise signal for training the network, the system can be more fault-tolerant.
Big data has emerged as an important area of study for both practitioners and researchers, reflecting the magnitude and impact of innovation in strategy and model in contemporary business organizations. In the paper, ...
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Big data has emerged as an important area of study for both practitioners and researchers, reflecting the magnitude and impact of innovation in strategy and model in contemporary business organizations. In the paper, data mining perspectives are pointed out about how business model innovation is driven by "key data" based on illustrations about big data. Nine basic factors can be represented as business model innovation. GA-bp model is constructed by the combination of genetic algorithm and bp algorithm to extract the knowledge from the data in data mining environment and to find associations, patterns by analyzing the big data sets. Finally, "key data" that affects the consequence significantly can be grabbed to explore entry points for business model innovation in the era of big data, and to offer enterprises and executives for business model innovation from a new version.
In the artificial neural network,bp neural network is a multilayer feedforward neural network which is used *** neural network uses a classic bp algorithm,and it is in accordance with the error back-propagation algori...
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In the artificial neural network,bp neural network is a multilayer feedforward neural network which is used *** neural network uses a classic bp algorithm,and it is in accordance with the error back-propagation algorithm for learning and *** paper first analyzes the basic idea of bp algorithm,and using bp neural network model and the flow chart to illustrate;then,introduces the disadvantage of bp algorithm,has slow convergence and easy to fall into local minimum point and other defects,and describes the current improvements methods,such as adding momentum item,introduce variable step method and other optimization methods,which effectively improve the convergence of bp algorithm,to avoid falling into local minimum point;finally,describes in detail the application of bp neural network in face *** research on bp neural network,which can be further development and application of bp networks play an important role.
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