The Infona portal uses cookies, i.e. strings of text saved by a browser on the user's device. The portal can access those files and use them to remember the user's data, such as their chosen settings (screen view, interface language, etc.), or their login data. By using the Infona portal the user accepts automatic saving and using this information for portal operation purposes. More information on the subject can be found in the Privacy Policy and Terms of Service. By closing this window the user confirms that they have read the information on cookie usage, and they accept the privacy policy and the way cookies are used by the portal. You can change the cookie settings in your browser.
Electromyography (EMG) signals can be used to integrate with machines and form one assistive system such as a powered exoskeleton. This paper focuses on the design and development of a low-cost elbow joint powered exoskeleton for human power augmentation, controlled by the EMG. Majority of the hardware has been designed and developed in-house, without using expensively available hardware. A theoretical...
In this paper, we introduce new, more efficient, methods for training recurrent neural networks (RNNs). These methods are based on a new understanding of the error surfaces of RNNs that has been developed in recent years. These error surfaces contain spurious valleys that disrupt the search for global minima. The spurious valleys are caused by instabilities in the networks, which become more pronounced...
Humans possess a strong innate predisposition to emotionally attach to familiar people around them who provide physical or emotional security. Attachment Theory describes and explains diverse phenomena related to this predisposition, including: infants using their carers as secure-bases from which to explore, and havens of safety to return to when tired or anxious, the development of attachment patterns...
Among different machine learning algorithms AdaBoost is a classification technique, which improves the classification accuracy by increasing the weights of the misclassified data. To overcome the problem of misclassification in Real AdaBoost algorithm, of the already classified samples, concept of margin is employed in the Parameterized AdaBoost algorithm. The new parameter, introduced in Parameterized...
Population based encodings allow to represent probabilistic and fuzzy state estimates. Such a representation will be introduced and applied for the case of a redundant manipulator. Following the Mean of Multiple Computations principle, a neural network model (PbMMC) is presented in which the overall complexity is divided into multiple local relationships. This allows to solve inverse, forward and...
We present an architecture for incremental online learning in high-dimensional feature spaces and apply it on a mobile robot. The model is based on learning vector quantization, approaching the stability-plasticity problem of incremental learning by adaptive insertions of representative vectors. We employ a cost-function-based learning vector quantization approach and introduce a new insertion strategy...
In this paper, we propose a novel event-triggered adaptive dynamic programming (ADP) method using only the input-output data. Event-triggered method is widely used for its computational efficiency capacity. Comparing with the traditional method which updates the controller periodically, the event-triggered method only updates the controller when it is necessary and therefore the computation is reduced...
Intention understanding is a basic requirement for human-machine interaction. Action classification and object affordance recognition are two possible ways to understand human intention. In this study, Multiple Timescale Recurrent Neural Network (MTRNN) is adapted to analyze human action. Supervised MTRNN, which is an extension of Continuous Timescale Recurrent Neural Network (CTRNN), is used for...
A minimalistic cognitive architecture called MANIC is presented. The MANIC architecture requires only three function approximating models, and one state machine. Even with so few major components, it is theoretically sufficient to achieve functional equivalence with all other cognitive architectures, and can be practically trained. Instead of seeking to trasfer architectural inspiration from biology...
Quantum neural network is a fledging research domain based on the merge of classical neural network and quantum computing. In this paper, we add the quantum effect to the classical hamming neural network algorithm in order to employ the advantages of quantum information to yield, finally, a novel quantum competitive learning algorithm. The proposed algorithm, called quantum hamming neural network...
The automatic design of control systems for multi-robot teams that operate in real time is not affordable with traditional evolutionary algorithms mainly due to the huge computational requirements they imply. Embodied Evolution (EE) is an evolutionary paradigm that aims to address this problem through the embodiment of the individuals that make up the population in the physical robots. The interest...
Autonomous adaptive agents acting in a real or simulated environment exhibit behavioral strategies governed by complex dynamics. In order to have a mathematical description of the behavior of a population of robots, their position is recorded while performing repetitive actions in interaction with the world. The time series is analyzed within the framework of dynamical system theory, using several...
This study presents a robotic application of neural associative memory-based control system that imparts online learning and predictive control strategies to a cost-effective quadrotor helicopter, the Parrot AR.Drone 2.0. The control system is extended with to tackle a fundamental and challenging problem for the quadcoptor: hovering control. The proposed system is based on self-organizing incremental...
Recently robots are being used more frequently as assistants in domestic scenarios. In this context we train an apprentice robot to perform a cleaning task using interactive reinforcement learning since it has been shown to be an efficient learning approach benefiting from human expertise for performing domestic tasks. The robotic agent obtains interactive feedback via a speech recognition system...
Robust appearance-based person re-identification can only be achieved by combining multiple diverse features describing the subject. Since individual features perform different, it is not trivial to combine them. Often this problem is bypassed by concatenating all feature vectors and learning a distance metric for the combined feature vector. However, to perform well, metric learning approaches need...
Set the date range to filter the displayed results. You can set a starting date, ending date or both. You can enter the dates manually or choose them from the calendar.