Search

Active Learning Method (ALM) is one of the powerful tools in soft computing and it is inspired by the human brain capabilities in approaching complicated problems. ALM, which is in essence an adaptive fuzzy learning algorithm, tries to model a Multi-Input Single-Output system with several single-input single-output subsystems. Each of these subsystems is then modeled by an ink drop spread (IDS) plane. IDS operator, which is the main processing engine of ALM, extracts two kinds of informative features, Narrow Path and Spread, from each IDS plane without complicated computations. These features from all IDS planes are then aggregated in the inference engine. Despite the great performance of ALM in different applications, an efficient hardware implementation has remained a challenge, which is mainly due to considerably high memory requirement of IDS operation. In this paper, in a novel approach to IDS operation, we propose an abstract representation of the IDS planes which minimizes the memory requirement and the computational cost, and consequently, benefits the hardware implementation in terms of area and speed. The proposed approach is fully compatible with memristor-crossbar implementation with an adaptive learning capability. Simpler learning algorithm and higher speed make our proposed algorithm suitable for applications where real-time process, low-cost and small implementation are of high priority. Applications in the classification of real-world datasets and function approximation are provided to confirm the effectiveness of the algorithm. Eventually, the paper concludes that the proposed computing structure provides a synergy between artificial neural networks and fuzzy domains. [ABSTRACT FROM AUTHOR]

Active Learning Method (ALM) is a powerful fuzzy soft computing tool, developed originally in order to promote an engineering realization of human brain. This algorithm, as a macro-level brain imitation, has been inspired by some behavioral specifications of human brain and active learning ability. ALM is an adaptive recursive fuzzy learning algorithm, in which a complex Multi Input, Multi Output system can be represented as a fuzzy combination of several Single-Input, Single-Output systems. SISO systems as associative layer of algorithm capture partial spatial knowledge of sample data space, and enable a granular knowledge resolution tuning mechanism through the learning process. The knowledge in each sub-system and its effectiveness in the whole system would be extracted by Ink Drop Spread in brief IDS operator and consolidated using a Fuzzy Rule Base (FRB), in order to acquire expert knowledge. In this paper we investigate ALM as a conspicuous classifier in different types of classification problems. Also, a new ALM architecture to actively analyze ill-balanced image patterns is proposed. Different types of data sets are used as a benchmark, including a remote sensing image classification problem, to evaluate the ALM Classifier (ALMC). With active pattern generation ability and knowledge resolution tuning, ALMC has been distinguished from many conventional classification tools especially for complex structures and image patterns analysis. This work demonstrates that ALMC is a good noise robust and active classifier, which is adaptively adjusted through structural evolution and pattern evaluation mechanism. These remarkable capabilities, along with its straightforward learning process, make ALMC as a convenient soft computing tool to use in different types of low dimensional pattern recognition problems. [ABSTRACT FROM AUTHOR]