Construction vehicle automation for high accuracy applications require information about the state of the machine, resulting in a fully sensitized system with precise kinematic parameters. Since the measurement of the...
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Construction vehicle automation for high accuracy applications require information about the state of the machine, resulting in a fully sensitized system with precise kinematic parameters. Since the measurement of these parameters contains uncertainties, accurate measurement of them is an expensive task. Automatic calibration of link parameters makes the task of kinematic parameter determination easier. This study reports a method for forward kinematic chain estimation of an excavator by bacterial programming (BP) based on randomly placed inertial navigation systems (INS) per segments with microelectromechanical sensors (MEMS) within. MEMS INS with fusion techniques provide increasing accuracy with outstanding resilience against harsh environment in a rigid housing. With known robot kinematic the tool orientation estimation can be made more accurate also the path can be planned. The unknown model structure and parameters are established and identified by BP without any a priori or given information about the device according to Denavit-Hartenberg (DH) transformation conventions. Fundamentals of this approach are described in detail and shown on simulated measurement results.
In this paper a method is proposed for constructing hierarchical-interpolative fuzzy rule bases in order to model black box systems defined by input-output pairs, i.e. to solve supervised machine learning problems. Th...
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ISBN:
(纸本)9781424473175
In this paper a method is proposed for constructing hierarchical-interpolative fuzzy rule bases in order to model black box systems defined by input-output pairs, i.e. to solve supervised machine learning problems. The resulting hierarchical rule base is the knowledge base, which is constructed by using evolutionary techniques, namely, Genetic and bacterial programming Algorithms. Applying hierarchical-interpolative fuzzy rule bases is an advanced way of reducing the complexity of the knowledge base, whereas evolutionary methods ensure a relatively efficient learning process. This is the reason of the investigation of this combination.
The design phase of B-spline neural networks is a highly computationally complex task. Existent heuristics have been found to be highly dependent on the initial conditions employed. Increasing interest in biologically...
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The design phase of B-spline neural networks is a highly computationally complex task. Existent heuristics have been found to be highly dependent on the initial conditions employed. Increasing interest in biologically inspired learning algorithms for control techniques such as Artificial Neural Networks and Fuzzy Systems is in progress. In this paper, the bacterial programming approach is presented, which is based on the replication of the microbial evolution phenomenon. This technique produces an efficient topology search, obtaining additionally more consistent solutions.
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