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

A neuro-fuzzy architecture for real-time applications

Neural networks and fuzzy expert systems perform the same task of functional mapping using entirely different approaches. Each approach has certain unique features. The ability to learn specific input-output mappings from large input/output data possibly corrupted by noise and the ability to adapt or continue learning are some important features of neural networks. Fuzzy expert systems are known for their ability to deal with fuzzy information and incomplete/imprecise data in a structured, logical way. Since both of these techniques implement the same task (that of functional mapping--we regard 'inferencing' as one specific category under this class), a fusion of the two concepts that retains their unique features while overcoming their individual drawbacks will have excellent applications in the real world. In this paper, we arrive at a new architecture by fusing the two concepts. The architecture has the trainability/adaptibility (based on input/output observations) property of the neural networks and the architectural features that are unique to fuzzy expert systems. It also does not require specific information such as fuzzy rules, defuzzification procedure used, etc., though any such information can be integrated into the architecture. We show that this architecture can provide better performance than is possible from a single two or three layer feedforward neural network. Further, we show that this new architecture can be used as an efficient vehicle for hardware implementation of complex fuzzy expert systems for real-time applications. A numerical example is provided to show the potential of this approach

Automated implementation of rule-based expert systems with neural networks for time-critical applications

In fault diagnosis, control and real-time monitoring, both timing and accuracy are critical for operators or machines to reach proper solutions or appropriate actions. Expert systems are becoming more popular in the manufacturing community for dealing with such problems. In recent years, neural networks have revived and their applications have spread to many areas of science and engineering. A method of using neural networks to implement rule-based expert systems for time-critical applications is discussed here. This method can convert a given rule-based system into a neural network with fixed weights and thresholds. The rules governing the translation are presented along with some examples. We also present the results of automated machine implementation of such networks from the given rule-base. This significantly simplifies the translation process to neural network expert systems from conventional rule-based systems. Results comparing the performance of the proposed approach based on neural networks vs. the classical approach are given. The possibility of very large scale integration (VLSI) realization of such neural network expert systems is also discussed

A new approach for designing self-organizing systems and application to adaptive control

There is tremendous interest in the design of intelligent machines capable of autonomous learning and skillful performance under complex environments. A major task in designing such systems is to make the system plastic and adaptive when presented with new and useful information and stable in response to irrelevant events. A great body of knowledge, based on neuro-physiological concepts, has evolved as a possible solution to this problem. Adaptive resonance theory (ART) is a classical example under this category. The system dynamics of an ART network is described by a set of differential equations with nonlinear functions. An approach for designing self-organizing networks characterized by nonlinear differential equations is proposed

Volterra Accentuated Non-Linear Dynamical Admittance (VANYA) to model Deforestation: An Exemplification from the Amazon Rainforest

Intelligent automation supports us against cyclones, droughts, and seismic events with recent technology advancements. Algorithmic learning has advanced fields like neuroscience, genetics, and human-computer interaction. Time-series data boosts progress. Challenges persist in adopting these approaches in traditional fields. Neural networks face comprehension and bias issues. AI's expansion across scientific areas is due to adaptable descriptors and combinatorial argumentation. This article focuses on modeling Forest loss using the VANYA Model, incorporating Prey Predator Dynamics. VANYA predicts forest cover, demonstrated on Amazon Rainforest data against other forecasters like Long Short-Term Memory, N-BEATS, RCN

On the conservation and management of marine turtles

There are five species of marine turtles occurring in and around Mandapam - Rameswaram; both in the Gulf of Mannar and Palk Bay regions. They are incidentally caught live in trawl net, shore-seine, drift-gill net and bottom-set gill net operations in this area. They are in great demand in the rural sector. Though, the fishermen are aware that these endangered animals are protected, often the profit motive make them to sell these turtles. Whenever, any incidence is brought to the notice of the Regional Centre of the Institute, attempts have been made to rescue and release them back to the sea as a conservation measure

Seasonal landings of oil sardine, Sardinella longiceps at Rameswaram, Pamban and Mandapam areas

Unusual and unprecedented landings of oil sardine, Sardinella langiceps were noticed at Rameswaram and Pamban during January and February 1992. The estimated catch of oil sardine at Rameswaram for January- February 1992 was 4,561 t. The pair trawlers contributed 4,244 t and fish trawlers 317 t. The C/E varied from 1.5 to 8 t. At Pamban the pair trawling during the period realised 741 t

Mass mortality of triggerfish Odonus niger (Rupell) along Dhanuskodi coast

On 3rd and 4th October 2007, several fishes of the species Odonus niger (locally known as Karuppu Klaathi ) were washed ashore along the coast for a stretch of nearly 4 km from Dhanuskodi check post to Arichumunai. On an average, 60 fishes were found lying in every 15 m of the stretch

On the stranding of sea cow Dugong dugon at Mandapam along the Gulf of Mannar coast.

A dead male sea cow Dugong dugon (Muller) measuring 194 cm in total length and about 125kg in weight was stranded along the Gulf of Mannar coast at Mandapam.The morphometric measurements of specimen were taken and identified to species level