A novel technique for joint angles trajectory tracking control with energy optimization is proposed for a biped robot with toe foot. For the task of climbing stairs by a 9-link biped model, an adaptive cycloid traject...
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A novel technique for joint angles trajectory tracking control with energy optimization is proposed for a biped robot with toe foot. For the task of climbing stairs by a 9-link biped model, an adaptive cycloid trajectory for the swing phase is planned as a function of the staircase rise/run ratio. We consider Zero Moment Point criteria for satisfying stability constraints. The paper is primarily divided into three sections: 1) Planning stable cycloid trajectory for the initial step and subsequent steps for climbing upstairs. We incorporate inverse kinematics using an unsupervised artificial neural network with a knot shifting procedure for jerk minimization. 2) Developing dynamics for toe-foot biped model using Lagrange formulation along with contact modeling using the spring-damper system. We propose neuralnetwork Temporal Quantized Lagrange Dynamics, which couples inverse kinematics neuralnetwork with dynamics. 3) Using Ant Colony Optimization to tune Proportional-Derivative controller and torso angle in order to minimize joint trajectory errors and total energy consumed. Three cases with variable staircase dimensions have been taken, and a comparison is made to validate the effectiveness of the proposed work. Generated patterns have been simulated in (c) Matlab and MuJoCo.
In this paper, we approach, using neural computation and ensemble systems, a pattern classification problem in fluorescence spectrometry, the resolution of difficult multi-fungicide mixtures (overlapping), specificall...
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In this paper, we approach, using neural computation and ensemble systems, a pattern classification problem in fluorescence spectrometry, the resolution of difficult multi-fungicide mixtures (overlapping), specifically the benzimidazole fungicides, benomyl, carbendazim, thiabendazole and fuberidazole. These fungicides are compounds of an important environmental interest. Because of this, from an analytical point of view, it is interesting to develop sensitive, selective and simple methods for their determination. Fluorescence spectrometry has proven to be a sensitive and selective technique for determination of many compounds of environmental interest, but in some cases it is not enough. HUMANN is a hierarchical, unsupervised, modular, adaptive neural net with high biological plausibility, which has shown to be suitable for identification of these fungicides and organo-chlorinated compounds of environmental interest. We propose two modular artificial intelligent systems, with a structure of pre-processing and processing stage, a multi-input HUMANN-based system, using multi-fluorescence spectra as input to the system, and a HUMANN-ensemble system. We analyze the optimal configuration of inputs and the ensemble in order to obtain better results. We study such figures as precision and sensitivity of the method. Our proposal is a smart, flexible and effective complementary method, which allows reducing the analytical and/or computational complexity of the analysis.
Two lithofacies and fluid discriminating seismic attributes are integrated using artificial Intelligence (AI) via unsupervised artificial neural network (UANN) to characterize the architecture of deep water turbidite ...
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Two lithofacies and fluid discriminating seismic attributes are integrated using artificial Intelligence (AI) via unsupervised artificial neural network (UANN) to characterize the architecture of deep water turbidite channels and submarine fan lobes across a hydrocarbon bearing reservoir within the Frem Field deep-water Niger Delta. A data-based approach including reservoir identification, environment of deposition prediction, seismic attribute analysis and finally UANN using the competitive learning algorithm (CLA) was used to match patterns from the two seismic attributes in order to reduce and capture uncertainties inherent with characterization of turbidite sands within the stratigraphic and structurally complex deep-water Niger Delta. One hydrocarbon bearing reservoir (Sand R001) with excellent reservoir quality was identified from the wireline logs interpretation after which gamma ray logs motifs as well as root mean square (RMS) amplitude and sweetness attributes imaging revealed the environment of deposition of the sand as an inner fan channel within a complex system of several channels and submarine fan lobes. Discreet facies map generated from the CLA enabled a better definition of the architecture, orientation and trend of the sand and lobate nature of the submarine fans lobes associated with the reservoir. The resulting output led to an enhanced characterization of the architectural patterns of the reservoir as well as associated deep-water facies in terms of reservoir architecture and orientation. The discreet facies map also revealed both northeast southwest and northwest southeast orientation of turbidite channels and submarine fan lobes and indicates the channels serves as feeders to the lobate submarine fan systems. The study has shown the efficacy of AI in enhancing deep water architectural patterns via pattern matching of facies and fluid related seismic attributes using CLA and thereby shows the method is effective in reducing uncertainties inher
ART-2 is a self-organized and unsupervised artificial neural network constructed from adaptive resonance theory which can be used to classify continuous active data. But we found that the network has just used the big...
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ISBN:
(纸本)0780393953
ART-2 is a self-organized and unsupervised artificial neural network constructed from adaptive resonance theory which can be used to classify continuous active data. But we found that the network has just used the big amplitude information during classifying the data by typical ART-2 network, especially the time series data. Therefore, we proposed an improved architecture based on typical ART-2. Then, we point out the superiority of improved ART-2 network over typical ART-2 network in theory. At last, a simulation is given to show the superiority.
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