Learning Opposites Using Neural Networks

Many research works have successfully extended algorithms such as evolutionary algorithms, reinforcement agents and neural networks using "opposition-based learning" (OBL). Two types of the "opposites" have been defined in the literature, namely \textit{type-I} and \textit{type-II}. The former are linear in nature and applicable to the variable space, hence easy to calculate. On the other hand, type-II opposites capture the "oppositeness" in the output space. In fact, type-I opposites are considered a special case of type-II opposites where inputs and outputs have a linear relationship. However, in many real-world problems, inputs and outputs do in fact exhibit a nonlinear relationship. Therefore, type-II opposites are expected to be better in capturing the sense of "opposition" in terms of the input-output relation. In the absence of any knowledge about the problem at hand, there seems to be no intuitive way to calculate the type-II opposites. In this paper, we introduce an approach to learn type-II opposites from the given inputs and their outputs using the artificial neural networks (ANNs). We first perform \emph{opposition mining} on the sample data, and then use the mined data to learn the relationship between input $x$ and its opposite $\breve{x}$. We have validated our algorithm using various benchmark functions to compare it against an evolving fuzzy inference approach that has been recently introduced. The results show the better performance of a neural approach to learn the opposites. This will create new possibilities for integrating oppositional schemes within existing algorithms promising a potential increase in convergence speed and/or accuracy.Comment: To appear in proceedings of the 23rd International Conference on Pattern Recognition (ICPR 2016), Cancun, Mexico, December 201

Learning Agent for a Heat-Pump Thermostat With a Set-Back Strategy Using Model-Free Reinforcement Learning

The conventional control paradigm for a heat pump with a less efficient auxiliary heating element is to keep its temperature set point constant during the day. This constant temperature set point ensures that the heat pump operates in its more efficient heat-pump mode and minimizes the risk of activating the less efficient auxiliary heating element. As an alternative to a constant set-point strategy, this paper proposes a learning agent for a thermostat with a set-back strategy. This set-back strategy relaxes the set-point temperature during convenient moments, e.g. when the occupants are not at home. Finding an optimal set-back strategy requires solving a sequential decision-making process under uncertainty, which presents two challenges. A first challenge is that for most residential buildings a description of the thermal characteristics of the building is unavailable and challenging to obtain. A second challenge is that the relevant information on the state, i.e. the building envelope, cannot be measured by the learning agent. In order to overcome these two challenges, our paper proposes an auto-encoder coupled with a batch reinforcement learning technique. The proposed approach is validated for two building types with different thermal characteristics for heating in the winter and cooling in the summer. The simulation results indicate that the proposed learning agent can reduce the energy consumption by 4-9% during 100 winter days and by 9-11% during 80 summer days compared to the conventional constant set-point strategyComment: Submitted to Energies - MDPI.co

Learning Opposites with Evolving Rules

The idea of opposition-based learning was introduced 10 years ago. Since then a noteworthy group of researchers has used some notions of oppositeness to improve existing optimization and learning algorithms. Among others, evolutionary algorithms, reinforcement agents, and neural networks have been reportedly extended into their opposition-based version to become faster and/or more accurate. However, most works still use a simple notion of opposites, namely linear (or type- I) opposition, that for each $x\in[a,b]$ assigns its opposite as $\breve{x}_I=a+b-x$. This, of course, is a very naive estimate of the actual or true (non-linear) opposite $\breve{x}_{II}$, which has been called type-II opposite in literature. In absence of any knowledge about a function $y=f(\mathbf{x})$ that we need to approximate, there seems to be no alternative to the naivety of type-I opposition if one intents to utilize oppositional concepts. But the question is if we can receive some level of accuracy increase and time savings by using the naive opposite estimate $\breve{x}_I$ according to all reports in literature, what would we be able to gain, in terms of even higher accuracies and more reduction in computational complexity, if we would generate and employ true opposites? This work introduces an approach to approximate type-II opposites using evolving fuzzy rules when we first perform opposition mining. We show with multiple examples that learning true opposites is possible when we mine the opposites from the training data to subsequently approximate $\breve{x}_{II}=f(\mathbf{x},y)$.Comment: Accepted for publication in The 2015 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2015), August 2-5, 2015, Istanbul, Turke

A Survey of Adaptive Resonance Theory Neural Network Models for Engineering Applications

This survey samples from the ever-growing family of adaptive resonance theory (ART) neural network models used to perform the three primary machine learning modalities, namely, unsupervised, supervised and reinforcement learning. It comprises a representative list from classic to modern ART models, thereby painting a general picture of the architectures developed by researchers over the past 30 years. The learning dynamics of these ART models are briefly described, and their distinctive characteristics such as code representation, long-term memory and corresponding geometric interpretation are discussed. Useful engineering properties of ART (speed, configurability, explainability, parallelization and hardware implementation) are examined along with current challenges. Finally, a compilation of online software libraries is provided. It is expected that this overview will be helpful to new and seasoned ART researchers

Individual And Ensemble Pattern Classification Models Using Enhanced Fuzzy Min-Max Neural Networks

Pattern classification is one of the major components for the design and development of a computerized pattern recognition system. Focused on computational intelligence models, this thesis describes in-depth investigations on two possible directions to design robust and flexible pattern classification models with high performance. Firstly is by enhancing the learning algorithm of a neural-fuzzy network; and secondly by devising an ensemble model to combine the predictions from multiple neural-fuzzy networks using an agent-based framework. Owing to a number of salient features which include the ability of learning incrementally and establishing nonlinear decision boundary with hyperboxes, the Fuzzy Min-Max (FMM) network is selected as the backbone for designing useful and usable pattern classification models in this research. Two enhanced FMM variants, i.e. EFMM and EFMM2, are proposed to address a number of limitations in the original FMM learning algorithm. In EFMM, three heuristic rules are introduced to improve the hyperbox expansion, overlap test, and contraction processes. The network complexity and noise tolerance issues are undertaken in EFMM2. In addition, an agent-based framework is capitalized as a robust ensemble model to house multiple EFMM-based networks. A useful trust measurement method known as Certified Belief in Strength (CBS) is developed and incorporated into the ensemble model for exploiting the predictive performances of different EFMM-based networks

Neurofuzzy control to address stochastic variation in actuated-coordinated systems at closely-spaced intersections

This dissertation documents a method of addressing stochastic variation at closely-spaced signalized intersections using neurofuzzy control. Developed on the conventional actuated-coordinated control system, the neurofuzzy traffic signal control keeps the advantage of the conventional control system. Beyond this, the neurofuzzy signal control coordinates the coordinated phase with one of the non-coordinated phases with no reduction of the green band assigned to the coordination along the arterial, reduces variations of traffic signal times in the cycle caused by early return to green , hence, makes more sufficient utilization of green time at closely-spaced intersections. The neurofuzzy signal control system manages a non-coordinated movement in order to manage queue spillbacks and variations of signal timings.Specifically, the neurofuzzy controller establishes a secondary coordination between the upstream coordinated phase (through phase) and the downstream non-coordinated phase (left turn phase) based on real-time traffic demand. Under the fuzzy logic signal control, the traffic from the upstream intersection can arrive and join the queue at the downstream left turn lane and be served, and hence, less possibly be blocked on the downstream left turn lane. This secondary coordination favors left turn progression and, hence, reduces the queue spillbacks. The fuzzy logic method overcomes the natural disadvantage of currently widely used actuated-coordinated traffic signal control in that the fuzzy logic method could coordinate a coordinated movement with a non-coordinated movement. The experiment was conducted and evaluated using a simulation model created using the microscopic simulation program - VISSIM.The neurofuzzy control algorithm was coded with MATLAB which interacts with the traffic simulation model via VISSIM\u27s COM interface. The membership functions in the neurofuzzy signal control system were calibrated using reinforcement learning to further the performance. Comparisons were made between the trained neurofuzzy control, the untrained neurofuzzy control, and the conventional actuated-coordinated control under five different traffic volumes. The simulation results indicated that the trained neurofuzzy signal control outperformed the other two for each traffic case. Comparing to the conventional actuated-coordinated control, the trained neurofuzzy signal control reduced the average delay by 7% and the average number of stops by 6% under the original traffic volume; as traffic volume increasing to 120%, the reductions doubled