This paper considers the different parameters of multi layer perceptron architectures and suggests a suitable architecture to complete a specific data...
Perceptron is a method of neural network in computer sience. Perceptron is linear method.
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Artificial neural network play an important role in VLSI circuit to find and diagnosis multiple fault in digital circuit. In this paper, the example of single layer and multi-layer neural ne…Full description
Multi-layer perceptrons: Selection of a multi-layer perceptron for a specific data classification task Warren Gauci Abstract – This paper considers the different parameters of multi layer perceptron architectures and suggests a suitable architecture to complete a specific data set classification task. Results obtained from this paper are based on Math Works Neural Network Toolbox software. Rigorous testing with ariable hidden layer si!e" learning rate and tests sets lead to a neural architecture with the best performance measures. All testing was performed on an #ris $ata set. %erformance measures are ealuated by the use of the confusion matrix" the mean s&uare error plot and the receier operating characteristic chart. This paper will contribute to further adancements in the field of neuron training and in the field of distinguishing and classifying linearly and non'linearly separable data.
All weighted inputs are then added together and if they e$ceed a pre-set threshold "alue# the neuron fires! An ad,ustment to the M model lead to the formulation of the perceptron.# a term coined y /rank 0osenlatt! A perceptron is an M model with additional# fi$ed# pre processing! &his paper deals with perceptron architectures structure# as this kind of neuron is est for pattern recognition (see 123)! erceptrons may e grouped in single layer or multilayer architectures! Single layer architectures are restricted in classifying only linearly separale data# thus in this paper only multi layer perceptron (M4) networks are used# as the connection form one layer to the ne$t allows for non-linearly separale data recognition and classification! /or a comprehensi"e o"er"iew of other kinds of networks refer to 153!
&he ANN must e trained using a learning process! &his process in"ol"es the memorisation of patterns and the suse%uent response of the neural network! &his can e categorised into two paradigms' associati"e mapping and regularity detection! 4earning is performed y the updating of the "alue of weights associated with each input! &he methodology used in this paper makes use of an adapti"e network# in which neurons found in the input layer are capale of changing their weights! &he adapti"e network is introduced to a super"ised learning procedure# where each neuron actually knows the target output and ad,usts weight to the input signals to minimise the error! &he error is stipulated using a least mean s%uare con"ergence techni%ue! &he eha"iour of an ANN depends also on the input-output transfer function# which is specified for the units! &his paper makes use of sigmoid units# where the output "aries continuously ut not linearly as the input changes! Sigmoid units ear a greater resemlance to real neurons than linear units do! 6n order to train the ANN to perform a cl sifi ti task kind of ight
toolo$ allowed for the di"ision of the data set into training# "alidation and test sets! &he function that changes the numer of neurons in the hidden layer was used to change the M4 architecture! &he performance of the different ANN.s was assessed using the performance plots pro"ided y this software! '." (T(SET
&he dataset used for classification is an 6ris ata set! reated y 0!A /isher# this data set is a classic in the field of pattern recognition! 6t contains 9 classes of ;< instances each! +ach class refers to a type of 6ris plant' Setosa# =ersicolour# =irginica! >ne class is linearly separale from the other two# while the latter are not linearly separale from each other! +ach class has four attriutes' sepal length# sepal width# petal length# petal width! 4. METO
&he est structure of M4 to perform the gi"en classification task was determined
Dpload the est sample for each hidden layer si7e from the collection of data method' etermine the est o"erall sample and • the corresponding hidden layer si7e (using a"erage standard de"iation functions)' Work out another 2< samples using the • est determined B4 si7e' Select the est sample o"erall using • "alidation and test performance plots' Sa"e parameters of est sample and try • this ANN architecture on a new set of data! &his section of the method allowed for the determination of the o"erall est ANN architecture using another set of samples! . RES!)TS •
&he most rele"ant results are taulated in Table 1! All results were otained using the following percentage ratios for training# "alidation and test
7. ISC!SSION
6t may e concluded that although results are not always satisfactory# consistency is present only in consideraly small si7ed B4 networks! /urthermore# results show that class 5 and 9 are the classes containing non-linearly separale data! 6t may also e concluded that a specific M4 architecture for a particular classification task can e chosen# ut classification in random and not always consistent!
8. REERENCES
123 M! Nrgaard# >! 0a"n# N! oulsen# and 4! Bansen# Neural Networks for Modelling and ontrol of ynamic Systems#M! Grimle and M! Hohnson# +ds! 4ondon: Springer-=erlag# 5<<
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