26 research outputs found
Curvature-based sparse rule base generation for fuzzy rule interpolation
Fuzzy logic has been successfully widely utilised in many real-world applications. The most common application of fuzzy logic is the rule-based fuzzy inference system, which is composed of mainly two parts including an inference engine and a fuzzy rule base. Conventional fuzzy inference systems always require a rule base that fully covers the entire problem domain (i.e., a dense rule base). Fuzzy rule interpolation (FRI) makes inference possible with sparse rule bases which may not cover some parts of the problem domain (i.e., a sparse rule base). In addition to extending the applicability of fuzzy inference systems, fuzzy interpolation can also be used to reduce system complexity for over-complex fuzzy inference systems. There are typically two methods to generate fuzzy rule bases, i.e., the knowledge driven and data-driven approaches. Almost all of these approaches only target dense rule bases for conventional fuzzy inference systems. The knowledge-driven methods may be negatively affected by the limited availability of expert knowledge and expert knowledge may be subjective, whilst redundancy often exists in fuzzy rule-based models that are acquired from numerical data. Note that various rule base reduction approaches have been proposed, but they are all based on certain similarity measures and are likely to cause performance deterioration along with the size reduction.
This project, for the first time, innovatively applies curvature values to distinguish important features and instances in a dataset, to support the construction of a neat and concise sparse rule base for fuzzy rule interpolation. In addition to working in a three-dimensional problem space, the work also extends the natural three-dimensional curvature calculation to problems with high dimensions, which greatly broadens the applicability of the proposed approach. As a result, the proposed approach alleviates the âcurse of dimensionalityâ and helps to reduce the computational cost for fuzzy inference systems. The proposed approach has been validated and evaluated by three real-world applications. The experimental results demonstrate that the proposed approach is able to generate sparse rule bases with less rules but resulting in better performance, which confirms the power of the proposed system. In addition to fuzzy rule interpolation, the proposed curvature-based approach can also be readily used as a general feature selection tool to work with other machine learning approaches, such as classifiers
Fuzzy Controllers
Trying to meet the requirements in the field, present book treats different fuzzy control architectures both in terms of the theoretical design and in terms of comparative validation studies in various applications, numerically simulated or experimentally developed. Through the subject matter and through the inter and multidisciplinary content, this book is addressed mainly to the researchers, doctoral students and students interested in developing new applications of intelligent control, but also to the people who want to become familiar with the control concepts based on fuzzy techniques. Bibliographic resources used to perform the work includes books and articles of present interest in the field, published in prestigious journals and publishing houses, and websites dedicated to various applications of fuzzy control. Its structure and the presented studies include the book in the category of those who make a direct connection between theoretical developments and practical applications, thereby constituting a real support for the specialists in artificial intelligence, modelling and control fields
Dynamic non-linear system modelling using wavelet-based soft computing techniques
The enormous number of complex systems results in the necessity of high-level and cost-efficient
modelling structures for the operators and system designers. Model-based approaches offer a very
challenging way to integrate a priori knowledge into the procedure. Soft computing based models
in particular, can successfully be applied in cases of highly nonlinear problems. A further reason
for dealing with so called soft computational model based techniques is that in real-world cases,
many times only partial, uncertain and/or inaccurate data is available.
Wavelet-Based soft computing techniques are considered, as one of the latest trends in system
identification/modelling. This thesis provides a comprehensive synopsis of the main wavelet-based
approaches to model the non-linear dynamical systems in real world problems in conjunction with
possible twists and novelties aiming for more accurate and less complex modelling structure.
Initially, an on-line structure and parameter design has been considered in an adaptive Neuro-
Fuzzy (NF) scheme. The problem of redundant membership functions and consequently fuzzy
rules is circumvented by applying an adaptive structure. The growth of a special type of Fungus
(Monascus ruber van Tieghem) is examined against several other approaches for further
justification of the proposed methodology.
By extending the line of research, two Morlet Wavelet Neural Network (WNN) structures have
been introduced. Increasing the accuracy and decreasing the computational cost are both the
primary targets of proposed novelties. Modifying the synoptic weights by replacing them with
Linear Combination Weights (LCW) and also imposing a Hybrid Learning Algorithm (HLA)
comprising of Gradient Descent (GD) and Recursive Least Square (RLS), are the tools utilised for
the above challenges. These two models differ from the point of view of structure while they share
the same HLA scheme. The second approach contains an additional Multiplication layer, plus its
hidden layer contains several sub-WNNs for each input dimension. The practical superiority of
these extensions is demonstrated by simulation and experimental results on real non-linear
dynamic system; Listeria Monocytogenes survival curves in Ultra-High Temperature (UHT)
whole milk, and consolidated with comprehensive comparison with other suggested schemes.
At the next stage, the extended clustering-based fuzzy version of the proposed WNN schemes, is
presented as the ultimate structure in this thesis. The proposed Fuzzy Wavelet Neural network
(FWNN) benefitted from Gaussian Mixture Models (GMMs) clustering feature, updated by a
modified Expectation-Maximization (EM) algorithm. One of the main aims of this thesis is to illustrate how the GMM-EM scheme could be used not only for detecting useful knowledge from
the data by building accurate regression, but also for the identification of complex systems.
The structure of FWNN is based on the basis of fuzzy rules including wavelet functions in the
consequent parts of rules. In order to improve the function approximation accuracy and general
capability of the FWNN system, an efficient hybrid learning approach is used to adjust the
parameters of dilation, translation, weights, and membership. Extended Kalman Filter (EKF) is
employed for wavelet parameters adjustment together with Weighted Least Square (WLS) which
is dedicated for the Linear Combination Weights fine-tuning. The results of a real-world
application of Short Time Load Forecasting (STLF) further re-enforced the plausibility of the
above technique
Big-Data Science in Porous Materials: Materials Genomics and Machine Learning
By combining metal nodes with organic linkers we can potentially synthesize
millions of possible metal organic frameworks (MOFs). At present, we have
libraries of over ten thousand synthesized materials and millions of in-silico
predicted materials. The fact that we have so many materials opens many
exciting avenues to tailor make a material that is optimal for a given
application. However, from an experimental and computational point of view we
simply have too many materials to screen using brute-force techniques. In this
review, we show that having so many materials allows us to use big-data methods
as a powerful technique to study these materials and to discover complex
correlations. The first part of the review gives an introduction to the
principles of big-data science. We emphasize the importance of data collection,
methods to augment small data sets, how to select appropriate training sets. An
important part of this review are the different approaches that are used to
represent these materials in feature space. The review also includes a general
overview of the different ML techniques, but as most applications in porous
materials use supervised ML our review is focused on the different approaches
for supervised ML. In particular, we review the different method to optimize
the ML process and how to quantify the performance of the different methods. In
the second part, we review how the different approaches of ML have been applied
to porous materials. In particular, we discuss applications in the field of gas
storage and separation, the stability of these materials, their electronic
properties, and their synthesis. The range of topics illustrates the large
variety of topics that can be studied with big-data science. Given the
increasing interest of the scientific community in ML, we expect this list to
rapidly expand in the coming years.Comment: Editorial changes (typos fixed, minor adjustments to figures