PURGE PERMISSIVES ( 15 Nos.)


LOTV AND HOTV CLOSE
ALL LONV AND ALL HONV CLOSE
DRUM LEVEL NOT HIGH (<125)AND NOT LOW( <-125)
NO BOILER TRIP CONDITION PRESENT
ALL SCANNERS SHOWS NO FLAME
AIR FLOW LESS THAN 40 %
AUX. AIR DAMPER MODULATED
WIND BOX TO FURNACE DO NOT VERY HIGH
ALL PA FANS OFF
ALL HAG CLOSE
ALL MILL AND ALL FEEDERS OFF
....etc

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Molecular machinery governing GABAergic neuron specification in the
cerebellum


Abstract

Although the cerebellum contains a relatively small variety of neurons, the molecular machinery governing neuronal
generation and/or subtype specification is still poorly understood. We identified a novel mutant mouse, cerebelless, which
lacks the entire cerebellar cortex but survives up to the adult stages. Analyses of its phenotypes and identification of its
responsible gene clarified that Ptf1a (pancreas transcrip ....etc

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Knowledge Representation and Reasoning Logics for Articial Intelligence



Introduction
Propositional logics conceptualize domains at, but not below, the level of propositions.
A proposition is an expression in some language
 that is true or false,
 whose negation makes sense,
 that can be believed or not,
 whose negation can be believed or not,
 that can be put in the frame
“I believe that it is not the case that .”
Some examples of p ....etc

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This article describes the implementation of A minimal neuron model, the Morris-Lecar model. It is is implemented on field programmable analog arrays (FPAAs). The work involves solving the differential equation describing the model. It is done in a way similar way a computer solves the same problem: numerically integrate the differential equation The approach here is to The arithmetic operations are performed on the voltage mode circuits of the FPAAs.This neuron is limited by the limitations on the configurability of the FPAAs. Similar models ....etc

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neural networks and fuzzy logic:history of development in neural networks principles,application of artificial neural networks.
provide ready notes on neural networks and fuzzy logic as soon as possible.... ....etc

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ABSTRACT:
This project presents the approach to the short term load forecasting using Artificial Neural Network (ANN) and Fuzzy Logic. Artificial Neural Networks (AAN) have recently been receiving considerable attention and a large number of publications concerning ANN-based short-term load forecasting (STLF) have appeared in the literature. Along with this Fuzzy Logic , also a part of Artificial Intelligence, is known to result in accurate predictions. In this paper, a model using Fuzzy Logic with the help of an ANN ....etc

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Fuzzy Logic and Fuzzy Sets 


History


Lotfi Zadeh, at the University of California at Berkeley, first presented fuzzy logic in the mid-1960's.

Zadeh developed fuzzy logic as a way of processing data. Instead of requiring a data element to be either a member or non-member of a set, he introduced the idea of partial set membership.

In 1974 Mamdani and Assilian used fuzzy logic to regulate a steam engine.

In 1985 researchers at Bell laboratories ....etc

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Hi am tasnim i would like to get details on difference between biological neuron and artificial neuron in tabular form ..My friend Justin said difference between biological neuron and artificial neuron in tabular form will be available here and now i am living at tangail and i last studied in the college/school mbstu and i need help on difference between biological neuron and artificial neuron. ....etc

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Knowledge Representation and Reasoning Logics for Articial Intelligence


Introduction
Propositional logics conceptualize domains at, but not below, the level of propositions.
A proposition is an expression in some language
 that is true or false,
 whose negation makes sense,
 that can be believed or not,
 whose negation can be believed or not,
 that can be put in the frame
“I believe that it is not the case that .”
Some examples of pro ....etc

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Fuzzy Logic
Philosophical approach
Decisions based on degree of truth
Is not a method for reasoning under uncertainty that probability
Crisp Facts distinct boundaries
Fuzzy Facts imprecise boundaries
Probability - incomplete facts
Example €œ Scout reporting an enemy
Two tanks at grid NV 54 (Crisp)
A few tanks at grid NV 54 (Fuzzy)
There might be 2 tanks at grid NV 54 (Probabilistic)
Apply to Computer Games
Can have different characteristics of players
Strength: strong, medium, weak
Aggressiveness: meek, ....etc

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