Taguchi recomienda el uso de arreglos ortogonales para hacer matrices que contengan los controles y los factores de ruido en el diseño de experimentos. Taguchi method with Orthogonal Arrays reducing the sample size from. , to only seleccionó utilizando el método de Taguchi con arreglos ortogonales. Taguchi, el ingeniero que hizo los arreglos ortogonales posible con el fin de obtener productos robustos.

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In order to validate the network, 11 different cases were used. The input ee a neuron would be the weighted sum of its entire input links plus a bias or offset.

Unsupervised tagucho is used when only the inputs are known and the ANN organizes by itself in clusters of patterns.

The network was used to classify the 12 items from the ADOS-G tool algorithm into three levels of impact for Autism diagnosis: Remembering that values from the ANN output above or equal to 0.

The goal is to look for a reliable number of that could be used to train an ANN to generate a reliable diagnosis of ASD [31].

Evaluación de la Robustez del sistema Mahalanobis-Taguchi a diferentes Arreglos Factoriales.

Applying the chain rule for the change in the error as function of the output and the change in the output as a function of the changes in the input. Autism Spectrum Disorder ASD is the group of developmental disorders whose clinical profile includes a range of disorders in social interaction, communication, imagination and reduced and arreylos behavior [1].

Then using the ANN, arregloos tests were performed to classify the 12 areas within 3 ranges of impact: The number of cases for the network training data was determined by using the Taguchi method with Orthogonal Arrays reducing the sample size fromto only The problem with ortlgonales evaluation is that all areas are weighted equally; as long as the sums achieve the set points Autism is diagnosed.


ASD is a world health problem described for the first time in by Kanner [2]. It can be said that Showing, Shared enjoyment in Interaction and Frequency of vocalization directed to others arrelgos the three items of high impact for Autism detection. There are two main topologies for ANN: Faced with the challenge of characterizing or measuring symptoms and locate a patient at a functioning level, the ADOS -G has the advantage, with its variety of tasks, to make a diagnosis on observational basis.

Genichi Taguchi by Alfonso Armendariz on Prezi

These results yield to a sensitivity of 1 and specificity of 1. Wing, “The autistic spectrum”, The lancet,pp. It can be observed that most of these methods have used a large sample size in orttogonales to train their models and none of them have tried to minimize the sample size. The complete methodology is represented as a flow diagram in Figure 6.

This algorithm evaluation is shown as the last column in Table 4. This Orthogonal array is used as the selected data to train the ANN.

Different modules and tasks of the test are mainly oriented towards evaluating the level of communication and specific behaviors in social interactions. Juan N Navarro, It is clear that “definitely abnormal” in two areas is not exactly the same as “mildly abnormal” in four areas since mildly abnormal could be easier to overcome than a definitely abnormal.

D Robins, et al. The medium impact items are Stereotyped use of Words or Phrases, Unusual eye contact, Use of other’s body to communicate, Pointing, Facial expression directed to others and Response to joint attention.

Van Nostrand Reinhold,pp. The summed squared error is the E given by where E p is the error on pattern p. This classification was compared to the work done by [28].

For this, different levels of fractional factorial design 29 were used, as well as all possible fractions for each level to find out if the results varied depending on tagychi array utilized. The ANN was trained using the back-propagation method and it consists of 3 layers, the input layer has 40 neurons, the hidden layer has 60 and the output layer has 1 neuron see Figure 4.


It includes red flags for activities that the child had not developed at specific ages as well as screening tools such as questionnaires. Feed-forward Networks have been used for a great variety of medical applications such as diagnosis of appendicitis, dementia, myocardial infarction, pulmonary embolism, back pain and skin disorders among others [24].

The population used to train the system consisted of individuals with autism and 15 individual without autism, cases were used to verify it reaching an accuracy of Van Der Smagt Since both inputs and desired outputs are available, a supervised artificial neural network was created using Matlab ortognoales [32].

It was found that the items “Showing”, “Shared enjoyment in Interaction” and “Frequency of vocalization directed to others”, are the areas of highest impact for Autism diagnosis. Applying the chain rule Where Defining an update rule Where Applying the tagjchi rule for the change in the error as function of the output and the change in the output as a function of the changes in the input, Using the chain rule, when k is a hidden unit it is called h These yields to Increasing the number of hidden neurons can prevent from falling in a local minimum and diminish the error, but it might consist of a long training process [23].