The hierarchical fast learning artificial neural network (HieFLANN) is a clustering NN that can be initialized using statistical properties of the data set. This provides the possibility of constructing the entire network autonomously with no manual intervention. This distinguishes it from many existing networks that, though hierarchically plausible, still require manual initialization processes. The unique system of hierarchical networks begins with a reduction of the high-dimensional feature space into smaller and manageable ones. This process involves using the -iterations fast learning artificial neural network (KFLANN) to systematically cluster a square matrix containing the Mahalanobis distances (MDs) between data set features, into homogeneous feature subspaces (HFSs). The KFLANN is used for its heuristic network initialization capabilities on a given data set and requires no supervision. Through the recurring use of the KFLANN and a second stage involving canonical correlation analysis (CCA), the HieFLANN is developed. Experimental results on several standard benchmark data sets indicate that the autonomous determination of the HFS provides a viable avenue for feasible partitioning of feature subspaces. When coupled with the network transformation process, the HieFLANN yields results showing accuracies comparable with available methods. This provides a new platform by which data sets with high-dimensional feature spaces can be systematically resolved and trained autonomously, alleviating the effects of the curse of dimensionality.Index Terms-Canonical correlation analysis (CCA), data presentation sequence sensitivity (DPSS), hierarchical neural networks (NNs), homogeneous feature subspaces, -iterations fast learning artificial neural network (KFLANN).
This paper presents an implementation of place cells for a robot navigation using the K-iterations Fast Learning Artificial Neural Networks (KFLANN) clustering algorithm. The KFLANN possesses several desirable properties suitable for place cell robot navigation tasks. The technique proposed is able to autonomously adjust the resolution of cells according to the complexity of the environment. This is achieved through two parameters known as the tolerance and the vigilance of the network. In addition, a navigation system consisting of a topological map building and a place cell path planning strategy is presented. A physical implementation of the system was developed on an autonomous platform and actual results were obtained. The experimental results obtained indicate that the system was able to navigate successfully through the experimental space and also tolerate unexpected discrepancies arising from motor and sensor errors present in a real environment. Furthermore, despite abrupt changes in an environment due to the deliberate introduction of obstacles, the system was still able to cope without changes to the program. The experiment was also extended to include a kidnapped robot scenario and the results were favorable, indicating a positive use of allothetic cue recognition capabilities.
The human naturally possesses a robust and effective visual system that utilizes saccade and vergence eye movements to explore the visual environment. This article presents a system that provides the functional biological equivalent which consists of a pair of cameras that provide for saccade and vergence eye movements. Included in this article is a detailed description of a simplified equivalent of the saccade generation module (typically from the superior colliculus (SC)) based on a FLANN image segmentation method and a visual cortex (VC) equivalent model based on a hierarchical disparity estimation model for vergence control. These two models cooperate to provide the systematic means for the autonomous exploration of the scene. Combining saccade and vergence movements, we are able to selectively reconstruct the 3D relative positions of objects in the scene and segment the image of the object under vergence.
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