+1-415-670-9189

info@expertsmind.com

# Adaptive Fuzzy Petri Nets Assignment Help

###
Extensions Of PETRI NETS - Adaptive Fuzzy Petri Nets
**Adaptive Fuzzy Petri Nets**

An Adaptive Fuzzy Petri Nets is explained as a 9-tuple,

AFPN = {P, T , D, I , O, α, β, Th, W }

Here, Th: P → [0, 1] defines a function allocating a threshold value of λ_{i} from 0 to 1 to each place i. Hence we have,

Th = λ_{1} , λ_{2} , ... , λ_{n}

Now, for any type of transition t, the thresholds allocated to them then the transition is said to be enabled and can fire, if the certainty factors connected with the tokens of all its input places are greater.

Likewise, we have W explained as a set of W_{I} and W_{O},

W_{I}: I → [- 1, 1]

And,

W_{O}: O → [- 1, 1]

W_{O} and W_{I} are the set of output and input weights which allocates weights to all the arcs of the net W = W_{I} ∪ W_{O} . w_{ij} ∈ W_{I} denoted how much a place impacts a following transition connected by w_{ij}. Now a positive value of the factor means a positive impact and vice versa

Furthermore for any transition t, suppose I (t ) = { p_{1} , p_{2} , ... , p_{n} } .

The consequent input weights allocated to these places are as:

W_{I1} , w_{I2} , . . . , w_{In}

Now the sum of all the weights is unity such as:

w_{I1} + w_{I2} + .. . + w_{In} = 1

All these situation result in one subsequent, the total of the impact is 1. In the similar way, w_{Oj} ∈ W_{O }denoted that how much a transition impacts an output places whether it fires.

Here compared to the structure of the Fuzzy Petri Nets, the Adaptive Fuzzy Petri Nets combine the advantages of Fuzzy Petri Nets via having the simpler structure better description ability. The advantages and the differences between Fuzzy Petri Nets and Adaptive Fuzzy Petri Nets are given as:

(a) Th is explained as a mapping from each place to a threshold value in Adaptive Fuzzy Petri Nets quite than to a set of thresholds as in Fuzzy Petri Nets.

(b) The set of weights W in the Adaptive Fuzzy Petri Nets are composed of two parts, name is: a set of output weights and a set of input weights. An input weight is allocated to an arc from a place to a transition and an output weight is allocated to an arc from a transition to a place. In Adaptive Fuzzy Petri Nets the weights are added to arcs upon the other hand they are added to places.

(c) The unspecific reasoning process in Adaptive Fuzzy Petri Nets integrates the impact of each weighted branch. Conversely, the reasoning process is considers simply as the "more or less form" in the Fuzzy Petri Nets

(a) In Adaptive Fuzzy Petri Nets we consider even the negative weights while the FPNs were merely considering the positive ones.

Expertsmind’s world class education services

We at www.expertsmind.com offer email based assignment help – homework help and projects assistance from k-12 academic level to college and university level and management and engineering studies. Our experts are helping students in their studies and they offer instant tutoring assistance giving their best practiced knowledge and spreading their world class education services through e-Learning program.

- Quality assignment help assistance 24x7 hrs

- Best qualified tutor’s network

- Time on delivery

- Quality assurance before delivery

- 100% originality and fresh work

**Adaptive Fuzzy Petri Nets**

_{i}from 0 to 1 to each place i. Hence we have,

_{1}, λ

_{2}, ... , λ

_{n}

_{I}and W

_{O},

_{I}: I → [- 1, 1]

_{O}: O → [- 1, 1]

_{O}and W

_{I}are the set of output and input weights which allocates weights to all the arcs of the net W = W

_{I}∪ W

_{O}. w

_{ij}∈ W

_{I}denoted how much a place impacts a following transition connected by w

_{ij}. Now a positive value of the factor means a positive impact and vice versa

_{1}, p

_{2}, ... , p

_{n}} .

_{I1}, w

_{I2}, . . . , w

_{In}

_{I1}+ w

_{I2}+ .. . + w

_{In}= 1

_{Oj}∈ W

_{O }denoted that how much a transition impacts an output places whether it fires.