66,259 research outputs found
Neuro-fuzzy knowledge processing in intelligent learning environments for improved student diagnosis
In this paper, a neural network implementation for a fuzzy logic-based model of the diagnostic process is proposed as a means to achieve accurate student diagnosis and updates of the student model in Intelligent Learning Environments. The neuro-fuzzy synergy allows the diagnostic model to some extent "imitate" teachers in diagnosing students' characteristics, and equips the intelligent learning environment with reasoning capabilities that can be further used to drive pedagogical decisions depending on the student learning style. The neuro-fuzzy implementation helps to encode both structured and non-structured teachers' knowledge: when teachers' reasoning is available and well defined, it can be encoded in the form of fuzzy rules; when teachers' reasoning is not well defined but is available through practical examples illustrating their experience, then the networks can be trained to represent this experience. The proposed approach has been tested in diagnosing aspects of student's learning style in a discovery-learning environment that aims to help students to construct the concepts of vectors in physics and mathematics. The diagnosis outcomes of the model have been compared against the recommendations of a group of five experienced teachers, and the results produced by two alternative soft computing methods. The results of our pilot study show that the neuro-fuzzy model successfully manages the inherent uncertainty of the diagnostic process; especially for marginal cases, i.e. where it is very difficult, even for human tutors, to diagnose and accurately evaluate students by directly synthesizing subjective and, some times, conflicting judgments
A memristive nanoparticle/organic hybrid synapstor for neuro-inspired computing
A large effort is devoted to the research of new computing paradigms
associated to innovative nanotechnologies that should complement and/or propose
alternative solutions to the classical Von Neumann/CMOS association. Among
various propositions, Spiking Neural Network (SNN) seems a valid candidate. (i)
In terms of functions, SNN using relative spike timing for information coding
are deemed to be the most effective at taking inspiration from the brain to
allow fast and efficient processing of information for complex tasks in
recognition or classification. (ii) In terms of technology, SNN may be able to
benefit the most from nanodevices, because SNN architectures are intrinsically
tolerant to defective devices and performance variability. Here we demonstrate
Spike-Timing-Dependent Plasticity (STDP), a basic and primordial learning
function in the brain, with a new class of synapstor (synapse-transistor),
called Nanoparticle Organic Memory Field Effect Transistor (NOMFET). We show
that this learning function is obtained with a simple hybrid material made of
the self-assembly of gold nanoparticles and organic semiconductor thin films.
Beyond mimicking biological synapses, we also demonstrate how the shape of the
applied spikes can tailor the STDP learning function. Moreover, the experiments
and modeling show that this synapstor is a memristive device. Finally, these
synapstors are successfully coupled with a CMOS platform emulating the pre- and
post-synaptic neurons, and a behavioral macro-model is developed on usual
device simulator.Comment: A single pdf file, with the full paper and the supplementary
information; Adv. Func. Mater., on line Dec. 13 (2011
Sliding-mode neuro-controller for uncertain systems
In this paper, a method that allows for the merger of the good features of sliding-mode control and neural network (NN) design is presented. Design is performed by applying an NN to minimize the cost function that is selected to depend on the distance from the sliding-mode manifold, thus providing that the NN controller enforces sliding-mode motion in a closed-loop system. It has been proven that the selected cost function has no local minima in controller parameter space, so under certain conditions, selection of the NN weights guarantees that the global minimum is reached, and then the sliding-mode conditions are satisfied; thus, closed-loop motion is robust against parameter changes and disturbances. For controller design, the system states and the nominal value of the control input matrix are used. The design for both multiple-input-multiple-output and single-input-single-output systems is discussed. Due to the structure of the (M)ADALINE network used in control calculation, the proposed algorithm can also be interpreted as a sliding-mode-based control parameter adaptation scheme. The controller performance is verified by experimental results
A Developmental Neuro-Robotics Approach for Boosting the Recognition of Handwritten Digits
Developmental psychology and neuroimaging
research identified a close link between numbers and fingers,
which can boost the initial number knowledge in children. Recent
evidence shows that a simulation of the children's embodied
strategies can improve the machine intelligence too. This article
explores the application of embodied strategies to convolutional
neural network models in the context of developmental neurorobotics, where the training information is likely to be gradually
acquired while operating rather than being abundant and fully
available as the classical machine learning scenarios. The
experimental analyses show that the proprioceptive information
from the robot fingers can improve network accuracy in the
recognition of handwritten Arabic digits when training examples
and epochs are few. This result is comparable to brain imaging
and longitudinal studies with young children. In conclusion, these
findings also support the relevance of the embodiment in the case
of artificial agents’ training and show a possible way for the
humanization of the learning process, where the robotic body can
express the internal processes of artificial intelligence making it
more understandable for humans
Neurofly 2008 abstracts : the 12th European Drosophila neurobiology conference 6-10 September 2008 Wuerzburg, Germany
This volume consists of a collection of conference abstracts
Gating Artificial Neural Network Based Soft Sensor
This work proposes a novel approach to Soft Sensor modelling,
where the Soft Sensor is built by a set of experts which are artificial
neural networks with randomly generated topology. For each of
the experts a meta neural network is trained, the gating Artificial Neural
Network. The role of the gating network is to learn the performance of the
experts in dependency on the input data samples. The final prediction
of the Soft Sensor is a weighted sum of the individual experts predictions.
The proposed meta-learning method is evaluated on two different
process industry data sets
Multi-criteria Evolution of Neural Network Topologies: Balancing Experience and Performance in Autonomous Systems
Majority of Artificial Neural Network (ANN) implementations in autonomous
systems use a fixed/user-prescribed network topology, leading to sub-optimal
performance and low portability. The existing neuro-evolution of augmenting
topology or NEAT paradigm offers a powerful alternative by allowing the network
topology and the connection weights to be simultaneously optimized through an
evolutionary process. However, most NEAT implementations allow the
consideration of only a single objective. There also persists the question of
how to tractably introduce topological diversification that mitigates
overfitting to training scenarios. To address these gaps, this paper develops a
multi-objective neuro-evolution algorithm. While adopting the basic elements of
NEAT, important modifications are made to the selection, speciation, and
mutation processes. With the backdrop of small-robot path-planning
applications, an experience-gain criterion is derived to encapsulate the amount
of diverse local environment encountered by the system. This criterion
facilitates the evolution of genes that support exploration, thereby seeking to
generalize from a smaller set of mission scenarios than possible with
performance maximization alone. The effectiveness of the single-objective
(optimizing performance) and the multi-objective (optimizing performance and
experience-gain) neuro-evolution approaches are evaluated on two different
small-robot cases, with ANNs obtained by the multi-objective optimization
observed to provide superior performance in unseen scenarios
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