2,499 research outputs found
Bayesian perception of touch for control of robot emotion
In this paper, we present a Bayesian approach for
perception of touch and control of robot emotion. Touch is an
important sensing modality for the development of social robots,
and it is used in this work as stimulus through a human-robot
interaction. A Bayesian framework is proposed for perception of
various types of touch. This method together with a sequential
analysis approach allow the robot to accumulate evidence from
the interaction with humans to achieve accurate touch perception
for adaptable control of robot emotions. Facial expressions are
used to represent the emotions of the iCub humanoid. Emotions
in the robotic platform, based on facial expressions, are handled
by a control architecture that works with the output from the
touch perception process. We validate the accuracy of our system
with simulated and real robot touch experiments. Results from
this work show that our method is suitable and accurate for
perception of touch to control robot emotions, which is essential
for the development of sociable robots
Bayesian Multivariate Spatial Models for Lattice Data with INLA
The INLAMSM package for the R programming language provides a collection of
multivariate spatial models for lattice data that can be used with package INLA
for Bayesian inference. The multivariate spatial models include different
structures to model the spatial variation of the variables and the
between-variables variability. In this way, fitting multivariate spatial models
becomes faster and easier. The use of the different models included in the
package is illustrated using two different datasets: the well-known North
Carolina SIDS data and mortality by three causes of death in Comunidad
Valenciana (Spain)
Multilayer Fuzzy Extreme Learning Machine Applied to Active classification and Transport of objects using an Unmanned Aerial Vehicle
A combined Adaptive Neuro-Fuzzy and Bayesian strategy for recognition and prediction of gait events using wearable sensors
A robust strategy for recognition and prediction of gait events using wearable sensors is presented in this paper. The strategy adopted here uses a combination of two computational intelligence approaches: Adaptive Neuro-Fuzzy and Bayesian methods. Recognition of gait events is performed by a Bayesian method which iteratively accumulates evidence to reduce uncertainty from sensor measurements. Prediction of gait events is based on the observation of decisions and actions made over time by our perception system. An Adaptive Neuro-Fuzzy system evaluates the reliability of predictions, learns a weighting parameter and controls the amount of predicted information to be used by our Bayesian method. Thus, this strategy ensures the achievement of better recognition and prediction performance in both accuracy and speed. The methods are validated with experiments for recognition and prediction of gait events with different walking activities, using data from wearable sensors attached to lower limbs of participants. Overall, results show the benefits of our combined Adaptive Neuro-Fuzzy and Bayesian strategy to achieve fast and accurate decisions, but also to evaluate and adapt its own performance, making it suitable for the development of intelligent assistive and rehabilitation robots
Evolutionary extreme learning machine for the interval type-2 radial basis function neural network: A fuzzy modelling approach
Evolutionary Extreme Learning Machine (E-ELM) is frequently much more efficient than traditional gradient-based algorithms for the parameter identification of feedforward neural networks. In particular, E-ELM is usually faster and provides a higher trade-off between accuracy and model simplicity. For that reason, this paper shows that an E-ELM that is based on Particle Swarm Optimisation (PSO) and Extreme Learning machine (ELM) can be extended to the Interval Type-2 Radial Basis Function Neural Network (IT2-RBFNN) with a Karnik-Mendel type-reduction layer. To evaluate the efficiency of E-ELM, the IT2-RBFNN is used as an Interval Type-2 Fuzzy Logic System (IT2 FLS) for the modelling of two popular benchmark data sets and for the prediction of chaotic time series. According to our results, E-ELM applied to the IT2-RBFNN not only outperforms adaptive-gradient-based algorithms and provides a better generalisation compared to other existing IT2 fuzzy methodologies, but similarly to pure fuzzy models, the IT2-RBFNN is also able to preserve some model interpretation and transparency
Recognition of walking activity and prediction of gait periods with a CNN and first-order MC strategy
In this paper, a strategy for recognition of human walking activities and prediction of gait periods using wearable sensors is presented. First, a Convolutional Neural Network (CNN) is developed for the recognition of three walking activities (level-ground walking, ramp ascent and descent) and recognition of gait periods. Second, a first-order Markov Chain (MC) is employed for the prediction of gait periods, based on the observation of decisions made by the CNN for each walking activity. The validation of the proposed methods is performed using data from three inertial measurement units (IMU) attached to the lower limbs of participants. The results show that the CNN, together with the first-order MC, achieves mean accuracies of 100% and 98.32% for recognition of walking activities and gait periods, respectively. Prediction of gait periods are achieved with mean accuracies of 99.78%, 97.56% and 97.35% during level-ground walking, ramp ascent and descent, respectively. Overall, the benefits of our work for accurate recognition and prediction of walking activity and gait periods, make it a suitable high-level method for the development of intelligent assistive robots
General type-2 radial basis function neural network: a data-driven fuzzy model
This paper proposes a new General Type-2 Radial Basis Function Neural Network (GT2-RBFNN) that is functionally equivalent to a GT2 Fuzzy Logic System (FLS) of either Takagi-Sugeno-Kang (TSK) or Mamdani type. The neural structure of the GT2-RBFNN is based on the alpha-planes representation, in which the antecedent and consequent part of each fuzzy rule uses GT2 Fuzzy Sets (FSs). To reduce the iterative nature of the Karnik-Mendel algorithm, the Enhaned-Karnik-Mendel (EKM) type-reduction and three popular direct-defuzzification methods, namely the 1) Nie-Tan approach (NT), the 2) Wu-Mendel uncertain bounds method (WU) and the 3) Biglarbegian-Melek-Mendel algorithm (BMM) are employed. For that reason, this paper provides four different neural structures of the GT2-RBFNN and their structural and parametric optimisation. Such optimisation is a two-stage methodology that first implements an Iterative Information Granulation approach to estimate the antecedent parameters of each fuzzy rule. Secondly, each consequent part and the fuzzy rule base of the GT2-RBFNN is trained and optimised using an Adaptive Gradient Descent method (AGD) respectively. Several benchmark data sets, including a problem of identification of a nonlinear system and a chaotic time series are considered. The reported comparative analysis of experimental results is used to evaluate the performance of the suggested GT2 RBFNN with respect to other popular methodologies
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