A neural network architecture for implementation of expert systems for real time monitoring

Since neural networks have the advantages of massive parallelism and simple architecture, they are good tools for implementing real time expert systems. In a rule based expert system, the antecedents of rules are in the conjunctive or disjunctive form. We constructed a multilayer feedforward type network in which neurons represent AND or OR operations of rules. Further, we developed a translator which can automatically map a given rule base into the network. Also, we proposed a new and powerful yet flexible architecture that combines the advantages of both fuzzy expert systems and neural networks. This architecture uses the fuzzy logic concepts to separate input data domains into several smaller and overlapped regions. Rule-based expert systems for time critical applications using neural networks, the automated implementation of rule-based expert systems with neural nets, and fuzzy expert systems vs. neural nets are covered

Fuzzy Aggregators - an Overview

The article deals with mathematical formalism of the process of combining several inputs into a single output in fuzzy inteligent systems, the process known as aggregation. We are interested in logic aggregation operators. Such aggregators are present in most decision problems and in fuzzy expert systems. Fuzzy inteligent systems are equipped with aggregation operators (aggregators) with which reasoning models adapt well to human reasoning. A brief overview of the field of fuzzy aggregators is given. Attention is devoted to so called graded logic aggregators.. The role of fuzzy agregators in modelling reasoning and the way they are chosen in modelling are pointed out. The conclusions are given and research in the field is pointed out

A High Performance Fuzzy Logic Architecture for UAV Decision Making

The majority of Unmanned Aerial Vehicles (UAVs) in operation today are not truly autonomous, but are instead reliant on a remote human pilot. A high degree of autonomy can provide many advantages in terms of cost, operational resources and safety. However, one of the challenges involved in achieving autonomy is that of replicating the reasoning and decision making capabilities of a human pilot. One candidate method for providing this decision making capability is fuzzy logic. In this role, the fuzzy system must satisfy real-time constraints, process large quantities of data and relate to large knowledge bases. Consequently, there is a need for a generic, high performance fuzzy computation platform for UAV applications. Based on Lees’ [1] original work, a high performance fuzzy processing architecture, implemented in Field Programmable Gate Arrays (FPGAs), has been developed and is shown to outclass the performance of existing fuzzy processors

Beyond Material Implication: An Empirical Study of Residuum in Knowledge Enhanced Neural Networks

openKnowledge Enchanced Neural Networks (KENN) is a neuro-symbolic architecture that exploits fuzzy logic for injecting prior knowledge, codified by propositional formulas, into a neural network. It works by adding a new layer at the end of a generic neural network that further elaborates the initial predictions accordingly to the knowledge. In the existing KENN, according to material implication rule, a conditional statement is represented as a conjunctive normal form formula. The following work extends this interpretation of the implication by using the fuzzy logic's Residuum semantic and shows how it has been integrated into the original KENN architecture, while keeping it reproducible. The Residuum integration allowed to evaluate KENN on MNIST Addition, a task that couldn't be approached by the original architecture, and the results obtained were comparable to others state of the art neuro-symbolic methods. The extended architecture has subsequently been evaluated also on visual relationships detection, showing that it could improve the performance of the original one.Knowledge Enchanced Neural Networks (KENN) is a neuro-symbolic architecture that exploits fuzzy logic for injecting prior knowledge, codified by propositional formulas, into a neural network. It works by adding a new layer at the end of a generic neural network that further elaborates the initial predictions accordingly to the knowledge. In the existing KENN, according to material implication rule, a conditional statement is represented as a conjunctive normal form formula. The following work extends this interpretation of the implication by using the fuzzy logic's Residuum semantic and shows how it has been integrated into the original KENN architecture, while keeping it reproducible. The Residuum integration allowed to evaluate KENN on MNIST Addition, a task that couldn't be approached by the original architecture, and the results obtained were comparable to others state of the art neuro-symbolic methods. The extended architecture has subsequently been evaluated also on visual relationships detection, showing that it could improve the performance of the original one