Publications

It has been well established that fuzzy neurons and fuzzy neural networks (FNN) are highly adaptive to changing conditions, possessing robust learning capabilities and inherent transparency for supporting a high level of knowledge interpretability. Consequently, they have the potential to provide exceptional mechanisms for building intelligent systems that must operate in dynamic and rapidly changing environments. However, to fully exploit the potential of FNN structures and their parallel nature, efficient hardware implementation techniques need to be developed. Here we are concerned with their realization using "standard" digital hardware so they may appeal to a wide range of applications, and our objective in this study is to investigate this avenue and identify various critical design issues.

Granular/symbolic data processing is an emerging conceptual and computing paradigm of information processing. In the era of big data, the emergence of granular/symbolic processing has been motivated by the urgent need for intelligent transformation of empirical data that are now commonly available in vast quantities, into a human-manageable knowledge. In such an aggregation process, we hope to retain as much information as possible while making the findings easily understood and well-supported by the existing experimental evidence. Those aggregated entities are often referred to as symbolic or granular data. Research areas referred to as symbolic data analysis in statistics and multivariate data analysis address some of the fundamental or applied facets of granular computing. The theoretical fundamentals of granular/symbolic data processing are well-established. They involve set theory (interval mathematics), fuzzy sets, rough sets, and random sets linked together in a highly comprehensive treatment of this emerging paradigm. In addition to interval-based formalism of information granules, we also encounter histograms, distributions, lists of values, etc. Hence, granular/symbolic data processing hinges on a general computation theory that effectively uses granules such as classes, clusters, subsets, groups, and intervals to build an efficient computational model for complex applications realized in the presence of huge amounts of data, information, and knowledge. This research arises as a substantial shift from the current machine-centric to human-centric approach to information and knowledge.

Our reconfigurable fuzzy processor (RFP) implements both aggregative and referential operations. Its architecture combines structural and parametric flexibility in a network implementing RFPs as a collection of fuzzy neurons. A fuzzy neural network using a bidirectionally linked series of shared buses facilitates a modular and scalable design environment for the RFP. An appropriate interface, separate from the RFP neuron itself, promotes the reuse of the neuron design with alternative interconnection networks.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

In this article, we advocate that a knowledge tidbit residing in the output space could be helpful in improving the performance (accuracy) of the fuzzy rule-based model. It states that <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">if two outputs are far apart from each other</i> , <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">it is advisable to place their corresponding inputs in different clusters when forming subspaces of the input space</i> . Considering this knowledge guidance mechanism, we propose two different methods to partition the input space. In the first method, input data are first partitioned with the use of the standard clustering algorithm, say fuzzy C-means; here, a constructed partition matrix is reflective of the structure present in the input space. Then, the knowledge tidbit is used to adjust the entries of the original partition matrix in such a way that those input data whose corresponding output data are far apart from each other are assigned with low values of proximity. In the second method, we propose two strategies to modify the distance between input data and a prototype (cluster center) identified in the input space. The crux of this method is that if there are many input data (which, in virtue of the knowledge tidbit, are regarded as being far-apart from the input data of interest) around a certain prototype, the distance between the input data of interest and this prototype should be penalized. Thus, the membership of these input data to the prototype is reduced. The comprehensive experimental studies carried out on both synthetic and publicly available data are used to examine the usefulness of the proposed methods.