Download methods of artificial intelligence used in food industry optimization

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METHODS OF ARTIFICIAL INTELLIGENCE USED IN
FOOD INDUSTRY OPTIMIZATION
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Integrated Systems for Agri-food Production ISAP’03
We now look into the fundamental unit or building block of the artificial neural network, the neuron
(or processing element) itself. The processing element is also called an artificial neuron; this term,
however, is used here only with the understanding that it does not, even closely, describe the biological
neuron.
Figure 1 illustrates the workings of a neuron.
ABSTRACT: Lucrarea prezinta principalele posibilitati de aplicare a metodelor
inteligentei artificiale (Retele neuronale artificiale,sistem expert, logica Fuzzy, sisteme
evolutive, agenti inteligenti) in domeniul industriei alimentare.
KEY WORDS: Retele neuronale, logica Fuzzy, sisteme hibride, sisteme expert, artificial
inteligence (AI).
Figure 1.
1. INTRODUCTION
In the last ten years there are a lot of new methods of thinking, used in diferent
fields. Among them artificial inteligence technics are improving special in technics
and informatics. Food industry is a field in wich AI methods are very usefull. For
diferente targets there are diferent AI methods suitable to be used. For example
there are problems wich can not be converted in methemetical methods; in this
casethe most eficient method is ANN (artificial neural networks).
In other cases it is possible to build a knowledge base; so it is very useful an expert sistem.
2. ARTIFICIAL NEURAL NETWORKS CONCEPTS
The basic attributes of neural networks may be divided into the architecture and
the functional properties or neurodynamics. Architecture defines the network
structure, that is, the number of artificial neurons in the network and their
interconnectivity. Neural networks consist of many interconnected neurons, or
processing elements, with familiar characteristics, such as inputs, synaptic
strengths, activation, outputs, and bias. The neurodynamics of neural networks
defines their properties, that is, how the neural network learns, recalls, associates,
and continuously compares new information with existing knowledge, how it
classifies new information, and how it develops new classifications if necessary.
Neural networks process information but with a sequential algorithm. This
process is based on parallel decomposition of complex information into basic
elements. As composite color can be decomposed into fundamental wavelengths (or
frequencies) and amplitudes, then at any time, theoretically speaking, an exact color
may be reconstructed.
The purpose of the nonlinearly function is to ensure that the neuron’s response is
bounded-that is, the actual response of the neuron is conditioned, or damped, as a
result of large or small activating stimuli and thus is controllable. In the biological
word, conditioning of stimuli is continuously done by all sensory inputs. For
example it is well known that to perceive a sound as twice as loud an actual
increase in sound amplitude of about ten times must take place; hence, the almost
logarithmic response of the ear. Biological neurons condition their output response
in a similar manner, so this concept is consistent with the biological neuron. But the
nonlinearly function used in many paradigms is not necessarily a close replica of
the biological one; often it is merely used for mathematical convenience. Thus
different nonlinearly functions are used, depending on the paradigm and the
algorithm used.
3. ARTIFICIAL NEURAL NETWORK TOPOLOGIES
Artificial neural networks (ANN) comprise many neurons, interconnected in
certain ways to cast them into identifiable topologies. Some of the most used
topologies are illustrated in Figure 2.; Figure 3 (circles represent neurons). From
the figure one distinguishes single-layer and multi-layer networks. Typically, the
layer where the input patterns are applied is the input layer, the layer where the
output is obtained is the output layer, and the layers between the input and output
layers or the hidden layers. There may be one or more hidden layers, which are so
named because their outputs are not directly observable. Here is an ANN feed
forward and in Figure 3. we have a ANN totally connected feedforward:î
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Integrated Systems for Agri-food Production ISAP’03
If A 2 and/or B 2 , then H 22 .
Here „ and/or” signifis logical union or intersection the A’s and B’s are
fuzzified inputs, and the H’s are actions for each rule.
5. FUZZY NEURAL NETWORKS
Figure 2.
Figure 3.
4. FUZZY LOGIC
In Boolean logic the function of Boolean operators or gates AND, OR, and
INVERT is well know. For instance, by „gating” the value of two variables using an
AND, we get, 11 → 1, 10 → 0, 01 → 0, or 00 → 0. In fuzzy logic, the values are
not crisp, and their fuzziness exhibits a distribution described by the membership
function. Hence, it we try to „gate” two fuzzy variables, what will the output be?
This quiestion has been addressed by various fuzzy logics. Here, we consider minmax logic. In simple terms, if we consider union (equivalent to OR), the outcome is
equal to the input variable with the greatest value, max (x 1 , x 2 …,x n ). That is, if A
= 0.5, B= 0.7, and C=A OR B , then C=max (0.5, 0.7) = 0.7. If we consider
„intersection” ( equivalent to AND), the outcome is equal ti=0 the least value of the
input variables, min (x 1 , x 2 …,x n ). In this case, if C=A AND B, then C=min (0.5,
0.7)=0.5. If we consider „complement” (equivalent to NOT), then the outcome is
the complement of one, or x=1-x.If C=B, then, C=1-0.7= 0.3.
Example:
Consider the fuzzy logic expression µ A (x 1 ) AND µ B (x 2 ) evaluated at x 1 = 2
and x 2 = 4 to be µ A (x 1 = 2)=0.72 and µb (x 2 = 4) = 0.45. Thus, the output of the
expression is min {0.72, 0.45} = 0.45. Similarly, if the fuzzy expression was
µ A (x 1 ) OR µ B (x 2 ), then the output of the expression is max{0.75,0.45} = 0.72.
Furthermore, the complement of µ A (x 1 = 2) is 1 – 0.72 = 0.28.
In most fuzzy problems of the rules are generated based on past experience.
Concerning problems that deal with fuzzy engines or fuzzy control, one should
know all possible input-output relationships even in fuzzy terms.The input-output
relationships, or rules, are then easily expressed with if…then statements, such as:
If A 1 and/or B 1 , then H 11 , else .
If A 2 and/or B 1 , then H 21 , else.
If A 1 and/or B 2 , then H 12 , else.
We have seen that one of the characteristics of artificial neural nets is that they
can classify inputs. This is useful if plasticity is maintained; that is, the artificial
neuron netwotks (ANN) can continuously classify and also update classification.
We have studied the stability of ANNs and how robust ANNs are when inputs
become less defined (i.e, fuzzy inputs) or when some of the neurons do not function
properly (i.e.,fuzzy network parameters). In addition, we have seen that fuzzy
systems deal with current fuzzy information and are capable of providing crisp
outputs. However in fuzzy systems their is no learning and, even vaguely, the
input-output relationships – the fuzzy rules – must be known a priori.
In the fuzzy artificial neural network, the neural network part is primarily used for
its learning and classification capabilities and for pattern association and retrieval.
The neural network part automatically generates fuzzy logic rules and membership
functions during the training period. In addition, even after training , the neural
network keeps updating the membership functions and fuzzy logic rules as it learns
more and more from its input signals. Fuzzy logic, on the other hand, is used to
infer and provide a crisp or deffuzified output when fuzzy parameters exist.
6. OTHER METHODS
-hibrid systems
-expert systems
-artificial inteligence
7. REFERENCES
[1] Slavici T. - Calculatoare personale, Editura Mirton, Timsoara 1999
[2] Slavici T. - Elemente fundamentale ale proiectarii asistate de calculator, Editura
Eurobit, Timisoara 1999
[3] Haykin S. – “Neural Networks: A Comprehensive Foundation”, Second Edition,
IEEE Press 1999.
[4] Hertz J.,Krogh A.,Palmer R. – “Introduction to the Theory of Neural
Computation”Lectures Notes,Santa Fe Institute, Addison-Wesley Publishing
Company, 1995.