# INTRODUCTION TO ARTIFICIAL NEURAL NETWORKS SIVANANDAM PDF

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PDF | This document is written for newcomers in the field of artificial neural networks. This paper is written to introduce artificial neural networks with new comers from . 1) S.N Sivanandam, venarefeane.ga Raj, Introduction to. Introduction: Artificial Neural Networks are computational models inspired by Refrences: 1) S.N Sivanandam, venarefeane.ga Raj, Introduction to artificial neural. Introduction to Artificial Neural Networks. • What is an Artificial Neural Network? - It is a computational system inspired by the. Structure. Processing Method.

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venarefeane.gandam . Chapter 6 gives a brief introduction to genetic programming. Neural Network, Fuzzy Logic, Genetic Algorithm, Digital Control, Adaptive. This course introduces the basics of Neural Networks and essentials of Artificial venarefeane.gandam, venarefeane.gai, venarefeane.ga, “Introduction to. introduction to neural networks using matlab by sivanandam free download pdf: Find and download free study notes Neural Networks - Introduction to Artificial Intelligence - Lecture Slides in any universities, pdf fro free download.

These models require frequent sampling of blood, and can only partly capture the complexity associated with regulation of glucose.

## การอ้างอิงต่อปี

Here we present an improved clamp control algorithm which is motivated by the stochastic nature of glucose kinetics, while using the minimal need in blood samples required for evaluation of IR. A glucose pump control algorithm, based on artificial neural networks model was developed.

The system was trained with a data base collected from 62 rat model experiments, using a back-propagation Levenberg-Marquardt optimization.

Genetic algorithm was used to optimize network topology and learning features. The predictive value of the proposed algorithm during the temporal period of interest was significantly improved relative to a feedback control applied at an equivalent low sampling interval. Robustness to noise analysis demonstrates the applicability of the algorithm in realistic situations.

Introduction Insulin resistance syndrome IRS is one of the most widespread health problems in modern times, and is the main cause of type II diabetes. The clinical manifestations of the syndrome include abnormal plasma insulin levels, hypertension, dyslipidemia and glucose intolerance.

IRS is the main cause of type II diabetes and can also progress to obesity, cardiovascular disorders, non-alcoholic fatty liver disease, and polycystic ovary syndrome. Lifestyle habits, chronic use of certain medications [1] as well as genetic factors are assumed to be some of the pivotal causes of insulin resistance IR and type II diabetes.

Since no known and proven cure currently exists, treatment focuses on controlling the symptoms: regulation of blood glucose levels, control of weight, and maintenance of healthy blood fat levels [2]. In the United Nations has officially recognized diabetes as a global epidemic which requires allocation of resources for prevention and treatment. According to the World Health Organization WHO estimation, more than million people worldwide and around 20 million people in the US alone suffer from diabetes.

Due to the growing interest in treatment and prevention of IRS and type II diabetes, quantification of insulin resistance is critical for both clinical and research proposes.

## S N Sivanandam S Sumathi S N Deepa

The gold standard method for quantifying insulin resistance is the hyperinsulinemic-euglycemic glucose clamp HEGC technique. In this method, plasma glucose and plasma insulin concentrations are controlled by the investigator and thus the natural glucose-insulin feedback loop is interrupted and directed.

Robustness to noise analysis demonstrates the applicability of the algorithm in realistic situations. Introduction Insulin resistance syndrome IRS is one of the most widespread health problems in modern times, and is the main cause of type II diabetes.

The clinical manifestations of the syndrome include abnormal plasma insulin levels, hypertension, dyslipidemia and glucose intolerance. IRS is the main cause of type II diabetes and can also progress to obesity, cardiovascular disorders, non-alcoholic fatty liver disease, and polycystic ovary syndrome.

Lifestyle habits, chronic use of certain medications [1] as well as genetic factors are assumed to be some of the pivotal causes of insulin resistance IR and type II diabetes. Since no known and proven cure currently exists, treatment focuses on controlling the symptoms: regulation of blood glucose levels, control of weight, and maintenance of healthy blood fat levels [2].

In the United Nations has officially recognized diabetes as a global epidemic which requires allocation of resources for prevention and treatment. According to the World Health Organization WHO estimation, more than million people worldwide and around 20 million people in the US alone suffer from diabetes. Due to the growing interest in treatment and prevention of IRS and type II diabetes, quantification of insulin resistance is critical for both clinical and research proposes.

The gold standard method for quantifying insulin resistance is the hyperinsulinemic-euglycemic glucose clamp HEGC technique. In this method, plasma glucose and plasma insulin concentrations are controlled by the investigator and thus the natural glucose-insulin feedback loop is interrupted and directed. During the test, plasma insulin is raised acutely to a desired set-point and maintained at that level throughout the study due to constant exogenous insulin infusion.

Whole-body insulin resistance, can be calculated, under of the approximation of steady-state conditions of glucose and insulin levels. Thus, the exogenous glucose infusing rate GIR can serve as an estimation of the net glucose disposal rate Rd [5]. In order to maintain plasma glucose at the desired level, the HEGC test is performed such that the investigator manually sets the glucose infusion rate.

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To improve accuracy of this feedback loop, several real time computer-based algorithms have been developed for controlling glucose infusion rate in response to frequently measured plasma glucose levels. DeFronzo et al. If you check out the reverse-mode autodiff algorithm, you will find that the forward and reverse passes of backpropagation simply perform reverse-mode autodiff.

The last step of the backpropagation algorithm is a Gradient Descent step on all the connection weights in the network, using the error gradients measured earlier. This was essential because the step function contains only flat segments, so there is no gradient to work with Gradient Descent cannot move on a flat surface , while the logistic function has a well-defined nonzero derivative everywhere, allowing Gradient Descent to make some progress at every step.

The backpropagation algorithm may be used with other activation functions, instead of the logistic function. This often helps speed up convergence. However, in practice it works very well and has the advantage of being fast to compute. Activation functions and their derivatives An MLP is often used for classification, with each output corresponding to a different binary class e.

When the classes are exclusive e. The output of each neuron corresponds to the estimated probability of the corresponding class. Note that the signal flows only in one direction from the inputs to the outputs , so this architecture is an example of a feedforward neural network FNN.

A modern MLP including ReLU and softmax for classification Biological neurons seem to implement a roughly sigmoid S-shaped activation function, so researchers stuck to sigmoid functions for a very long time. This is one of the cases where the biological analogy was misleading. The DNNClassifier class makes it fairly easy to train a deep neural network with any number of hidden layers, and a softmax output layer to output estimated class probabilities.

Finally, we run 40, training iterations using batches of 50 instances. So the DNNClassifier class and any other contrib code may change without notice in the future. The output layer relies on the softmax function, and the cost function is cross entropy. The first step is the construction phase, building the TensorFlow graph.

The second step is the execution phase, where you actually run the graph to train the model. First we need to import the tensorflow library.

The shape of X is only partially defined.

We know that it will be a 2D tensor i. The placeholder X will act as the input layer; during the execution phase, it will be replaced with one training batch at a time note that all the instances in a training batch will be processed simultaneously by the neural network.

Now you need to create the two hidden layers and the output layer. The two hidden layers are almost identical: they differ only by the inputs they are connected to and by the number of neurons they contain. The output layer is also very similar, but it uses a softmax activation function instead of a ReLU activation function.

Variable tf. This is optional, but the graph will look much nicer in TensorBoard if its nodes are well organized. It will be initialized randomly, using a truncated 10 normal Gaussian distribution with a standard deviation of.

It is important to initialize connection weights randomly for all hidden layers to avoid any symmetries that the Gradient Descent algorithm would be unable to break. This vectorized implementation will efficiently compute the weighted sums of the inputs plus the bias term for each and every neuron in the layer, for all the instances in the batch in just one shot.

Note that adding a 1D array b to a 2D matrix with the same number of columns X. W results in adding the 1D array to every row in the matrix: this is called broadcasting. Finally, if an activation parameter is provided, such as tf.

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Okay, so now you have a nice function to create a neuron layer. The first hidden layer takes X as its input.

The second takes the output of the first hidden layer as its input. And finally, the output layer takes the output of the second hidden layer as its input.

Also note that logits is the output of the neural network before going through the softmax activation function: for optimization reasons, we will handle the softmax computation later. It takes care of creating the weights and biases variables, named kernel and bias respectively, using the appropriate initialization strategy, and you can set the activation function using the activation argument.

Simply replace the dnn construction section with the following code: with tf. We will use cross entropy.

As we discussed earlier, cross entropy will penalize models that estimate a low probability for the target class. TensorFlow provides several functions to compute cross entropy. This will give us a 1D tensor containing the cross entropy for each instance. This is why we did not apply the softmax activation function earlier. We will simply use accuracy as our performance measure. This returns a 1D tensor full of boolean values, so we need to cast these booleans to floats and then compute the average.

Saver Phew! This concludes the construction phase. This was fewer than 40 lines of code, but it was pretty intense: we created placeholders for the inputs and the targets, we created a function to build a neuron layer, we used it to create the DNN, we defined the cost function, we created an optimizer, and finally we defined the performance measure.

Now on to the execution phase. Execution Phase This part is much shorter and simpler.

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We could use Scikit-Learn for that, but TensorFlow offers its own helper that fetches the data, scales it between 0 and 1 , shuffles it, and provides a simple function to load one mini-batch a time. Moreover, the data is already split into a training set 55, instances , a validation set 5, instances , and a test set 10, instances.

Session as sess: init. Then it runs the main training loop: at each epoch, the code iterates through a number of mini-batches that corresponds to the training set size. Next, at the end of each epoch, the code evaluates the model on the last mini-batch and on the full validation set, and it prints out the result.

Finally, the model parameters are saved to disk. Using the Neural Network Now that the neural network is trained, you can use it to make predictions. To do that, you can reuse the same construction phase, but change the execution phase like this: with tf. Session as sess: saver. Then it loads some new images that you want to classify. Remember to apply the same feature scaling as for the training data in this case, scale it from 0 to 1.

Then the code evaluates the logits node.

If you wanted to know all the estimated class probabilities, you would need to apply the softmax function to the logits, but if you just want to predict a class, you can simply pick the class that has the highest logit value using the argmax function does the trick.This database included results obtained from previously reported experiments [10] , [11] , as well as results collected in experiments we have conducted and are reported here for the first time.

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