Local Adaptive Receptive Field Self-Organizing Map for Image Segmentation

A new self-organizing map with variable topology is introduced for image segmentation. The proposed network, called Local Adaptive Receptive Field Self-Organizing Map (LARFSOM-RBF), is a two-stage network capable of both color and border segment images. The color segmentation stage is responsibility of LARFSOM which is characterized by adaptive number of nodes, fast convergence and variable topology. For border segmentation RBF nodes are included to determine the border pixels using previously learned information of LARFSOM. LARFSOM-RBF was tested to segment images with different degrees of complexity showing promising results

Local Adaptive Receptive Field Self-Organizing Map for Image Segmentation

A new self-organizing map with variable topology is introduced for image segmentation. The proposed network, called Local Adaptive Receptive Field Self-Organizing Map (LARFSOM-RBF), is a two-stage network capable of both color and border segment images. The color segmentation stage is responsibility of LARFSOM which is characterized by adaptive number of nodes, fast convergence and variable topology. For border segmentation RBF nodes are included to determine the border pixels using previously learned information of LARFSOM. LARFSOM-RBF was tested to segment images with different degrees of complexity showing promising results

MOEA/D with Uniformly Randomly Adaptive Weights

When working with decomposition-based algorithms, an appropriate set of weights might improve quality of the final solution. A set of uniformly distributed weights usually leads to well-distributed solutions on a Pareto front. However, there are two main difficulties with this approach. Firstly, it may fail depending on the problem geometry. Secondly, the population size becomes not flexible as the number of objectives increases. In this paper, we propose the MOEA/D with Uniformly Randomly Adaptive Weights (MOEA/DURAW) which uses the Uniformly Randomly method as an approach to subproblems generation, allowing a flexible population size even when working with many objective problems. During the evolutionary process, MOEA/D-URAW adds and removes subproblems as a function of the sparsity level of the population. Moreover, instead of requiring assumptions about the Pareto front shape, our method adapts its weights to the shape of the problem during the evolutionary process. Experimental results using WFG41-48 problem classes, with different Pareto front shapes, shows that the present method presents better or equal results in 77.5% of the problems evaluated from 2 to 6 objectives when compared with state-of-the-art methods in the literature

Unsupervised Learning and Recall of Temporal Sequences: An Application to Robotics

This paper describes an unsupervised neural network model for learning and recall of temporal patterns. The model comprises two groups of synaptic weights, named competitive feedforward and Hebbian feedback, which are responsible for encoding the static and temporal features of the sequence respectively. Three additional mechanisms allow the network to deal with complex sequences: context units, a neuron commitment function, and redundancy in the representation of sequence states. The proposed network encodes a set of robot trajectories which may contain states in common, and retrieves them accurately in the correct order. Further tests evaluate the fault-tolerance and noise sensitivity of the proposed mode

Proceedings of the 14th IFAC World Congress, Beijing, China, July 5-9, pp. 373-378.

This paper proposes an unsupervised neural network for trajectory learning of a robotic arm. The neural network encodes trajectories by using competitive and temporal Hebbian learning rules and operates by producing the current and the next position for the robotic arm. Different types of trajectories can be learned independently of their complexity. Tests will focus on trajectories with one crossing point. The algorithm is able to reproduce the trajectories accurately and unambiguously due to context units used together with the input. Also, the proposed model is shown to be fault-tolerant and can respond well in the presence of noisy inputs. Copyright 1999 IFAC Keywords: Trajectory planning, robot control, neural networks, learning algorithms, faulttolerance

Proc. V Brazilian Symposium on Neural Networks (SBRN'98), Dec. 9-11, Belo Horizonte, MG, Vol. I, pp. 96-101, IEEE Press.

This paper proposes an unsupervised neural algorithm for trajectory production of a 6-DOF robotic arm. The model encodes these trajectories in a single training iteration by using competitive and temporal Hebbian learning rules and operates by producing the current and the next position for the robotic arm. In this paper we will focus on trajectories with one common point. These types of trajectories introduce some ambiguities, but even so, the neural algorithm is able to reproduce them accurately and unambiguously due to context units used as part of the input. In addition, the proposed model is shown to be fault-tolerant

Proc. ICSC/IEE Symposium on Engineering of Intelligent Systems, June 27-30, Paisley, Scotland, U. K., pp. 72-78.

We propose an unsupervised neural network model to learn and recall complex robot trajectories. Two cases are considered: (1) A single trajectory in which a particular arm configuration may occur more than once, and (2) trajectories sharing states with other ones -- they are said to contain a shared state. Hence, ambiguities occur in both cases during recall of such trajectories. The proposed model consists of two groups of synaptic weights trained by competitive and Hebbian learning laws. They are responsible for encoding spatial and temporal features of the input sequences, respectively. Three mechanisms allow the network to deal with repeated or shared states: local and global context units, neurons disabled to learn, and redundancy. The network produces the current and the next state of the learned sequences and is able to solve ambiguities. The model is simulated over various sets of robot trajectories in order to evaluate learning and recall, trajectory sampling effects and robustness