29,852 research outputs found
An Architecture-Altering and Training Methodology for Neural Logic Networks: Application in the Banking Sector
Artificial neural networks have been universally acknowledged for their ability on constructing forecasting and classifying systems. Among their desirable features, it has always been the interpretation of their structure, aiming to provide further knowledge for the domain experts. A number of methodologies have been developed for this reason. One such paradigm is the neural logic networks concept. Neural logic networks have been especially designed in order to enable the interpretation of their structure into a number of simple logical rules and they can be seen as a network representation of a logical rule base. Although powerful by their definition in this context, neural logic networks have performed poorly when used in approaches that required training from data. Standard training methods, such as the back-propagation, require the networkās synapse weight altering, which destroys the networkās interpretability. The methodology in this paper overcomes these problems and proposes an architecture-altering technique, which enables the production of highly antagonistic solutions while preserving any weight-related information. The implementation involves genetic programming using a grammar-guided training approach, in order to provide arbitrarily large and connected neural logic networks. The methodology is tested in a problem from the banking sector with encouraging results
Genetic Programming for Smart Phone Personalisation
Personalisation in smart phones requires adaptability to dynamic context
based on user mobility, application usage and sensor inputs. Current
personalisation approaches, which rely on static logic that is developed a
priori, do not provide sufficient adaptability to dynamic and unexpected
context. This paper proposes genetic programming (GP), which can evolve program
logic in realtime, as an online learning method to deal with the highly dynamic
context in smart phone personalisation. We introduce the concept of
collaborative smart phone personalisation through the GP Island Model, in order
to exploit shared context among co-located phone users and reduce convergence
time. We implement these concepts on real smartphones to demonstrate the
capability of personalisation through GP and to explore the benefits of the
Island Model. Our empirical evaluations on two example applications confirm
that the Island Model can reduce convergence time by up to two-thirds over
standalone GP personalisation.Comment: 43 pages, 11 figure
Discrete and fuzzy dynamical genetic programming in the XCSF learning classifier system
A number of representation schemes have been presented for use within
learning classifier systems, ranging from binary encodings to neural networks.
This paper presents results from an investigation into using discrete and fuzzy
dynamical system representations within the XCSF learning classifier system. In
particular, asynchronous random Boolean networks are used to represent the
traditional condition-action production system rules in the discrete case and
asynchronous fuzzy logic networks in the continuous-valued case. It is shown
possible to use self-adaptive, open-ended evolution to design an ensemble of
such dynamical systems within XCSF to solve a number of well-known test
problems
A Review on Energy Consumption Optimization Techniques in IoT Based Smart Building Environments
In recent years, due to the unnecessary wastage of electrical energy in
residential buildings, the requirement of energy optimization and user comfort
has gained vital importance. In the literature, various techniques have been
proposed addressing the energy optimization problem. The goal of each technique
was to maintain a balance between user comfort and energy requirements such
that the user can achieve the desired comfort level with the minimum amount of
energy consumption. Researchers have addressed the issue with the help of
different optimization algorithms and variations in the parameters to reduce
energy consumption. To the best of our knowledge, this problem is not solved
yet due to its challenging nature. The gap in the literature is due to the
advancements in the technology and drawbacks of the optimization algorithms and
the introduction of different new optimization algorithms. Further, many newly
proposed optimization algorithms which have produced better accuracy on the
benchmark instances but have not been applied yet for the optimization of
energy consumption in smart homes. In this paper, we have carried out a
detailed literature review of the techniques used for the optimization of
energy consumption and scheduling in smart homes. The detailed discussion has
been carried out on different factors contributing towards thermal comfort,
visual comfort, and air quality comfort. We have also reviewed the fog and edge
computing techniques used in smart homes
Intelligent systems in manufacturing: current developments and future prospects
Global competition and rapidly changing customer requirements are demanding increasing changes in manufacturing environments. Enterprises are required to constantly redesign their products and continuously reconfigure their manufacturing systems. Traditional approaches to manufacturing systems do not fully satisfy this new situation. Many authors have proposed that artificial intelligence will bring the flexibility and efficiency needed by manufacturing systems. This paper is a review of artificial intelligence techniques used in manufacturing systems. The paper first defines the components of a simplified intelligent manufacturing systems (IMS), the different Artificial Intelligence (AI) techniques to be considered and then shows how these AI techniques are used for the components of IMS
Genetic algorithm design of neural network and fuzzy logic controllers
Genetic algorithm design of neural network and fuzzy logic controller
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