Tuesday, 13 December 2011

Analytical Report Writing (Review of a research paper)

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Introduction and Description of the Document being analyzed

The technical document analyzed here is a research paper from Georgia Institute of Technology on the field of Artificial Intelligence and Neural Networks. The paper is titled Dynamic Neural Networks for output feedback control. It explains on the implementation of an innovative idea to control the complexities of the feedback control of the outputs from linear velocity vectors and displacements. The basic model is designed as a controller which implements a secondary “learning while controlling” neural network model. Also open and closed looped simulations are intended within a Van Der Pol oscillator and the results are demonstrated in a graphical manner, which makes them easy to understand and implement.

Basic review of the concepts of Neural Networks and Complex systems

To understand this document first let us review some of the basic concepts of how neural networks are implemented in control and learning atmosphere. Complex systems, for instance in industries and robotics, have a high amount of uncertainties and disturbances in them and hence they are not very easy to control and implement. As system uncertainty or performance requirements increase, it becomes necessary to augment traditional feedback controllers with additional feedback loops that effectively add intelligence to the system. Some theories of artificial intelligence (AI) are now showing how complex machine systems should mimic human cognitive and biological processes to improve their capabilities for dealing with uncertainty. In this research paper one such implementation in the field of aerospace navigation has been investigated and a novel solution to the problem has been suggested.

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Goals to achieve on successful completion and implementation of the project

Along with the implementation, this paper develops novel neural networks suitable for direct embedment within a feedback loop. The structure of the embedded system is inspired by the proportional plus derivative control with moving average excitations. A parameter vector based uniform description of the problem of neural network design is presented. Difficulties associated with traditional mathematically-guided design methods are discussed, which leads to the development of a genetic algorithm based evolutionary design method that overcomes these difficulties and makes direct neurocontrollers possible. Techniques are also developed to optimise the architecture in the same process of parameter training, leading to Darwin neural machines. The proposed methods are verified by examples of direct neurocontroller design for a delayed linear plant and a nonlinear plant.

This project if successfully completed can lead to a major breakthrough in combining the closed and the open loop navigation systems while providing an easy display and understanding of vectorial displacements under feedback output control. This application could be used to design airline navigation systems, defense tracking system and many other applications related to control and simulation of the feedback output. This paper is intended for the navigation technology people giving them a breakthrough by combining artificial intelligence into their implementations. If the project is successfully completed it will result in a new feedback output design and will open paths for further research into the field of vector dynamics and neural networks. It will combine the learning technology provided by neural nets into the excellent accuracy of trajectile vector dynamics.

Analysis of the different Section of the paper

The structure of the paper is well defined into different categories each specifying a step by step solution to the problem. The different sections of the paper are classified as

• Introduction,

• Neural network aided observer design,

• Neural Network based model inversion control,

• Simulation results and

• The appendix.

Let us consider in detail each part of this section and how it explains the material and its effectiveness.



The introduction of the paper is very well defined and gives the reader a lot of information into the insight and previous work performed in the field of neural networks. It also introduces the various variables and functions that have been used to explain the theory behind the project and also justifies their use. It lists the previous problems that has been existing the field so far and how neural networks will help and try to overpower the need for adaptive learning. Also the step by step progress in the introduction gives the paper a very good professional appearance.


The introduction is quite accurate in its information because it supports every statement that it makes by a mathematical theory that has been used in the field before. It introduces to the topic Honestly, is Concise in its nature, has a Professional Appearance, and is Accurate. The introduction is also designed in a very Comprehensive and elegant fashion to explain the whole basic concept to the reader.


Some of the determinants of the matrix used in the feedback and observer controlled process has not been defined in the Introduction. The Clarity of the paper is highly affected, because it confuses the reader when they are suddenly introduced in the later section. Also it makes the reader doubt the Correctness of the topic and affects the Accessibility between the various sections of the paper.

Neural network aided observer design

After the paper introduces the readers to the previous achievements and problems in the field of dynamic feedback control we move on to Neural network aided observer design.


In this section the authors give a complete description and pictorial understanding of how the neural networks will work with a aided observer. The vectors and variables for the section are highly understandable. Also the figure is simplified so that the readers can have a better overall understanding of the topic. The frobenius and the ODE varibales are used to develop the basic determinant matrix that helps to load the initiation of the observer model and pass feedback to the main controller. The observer in this form cannot be implemented since it requires the knowledge of X variable, which is not available at this moment.


This section is one of the most important because it helps to understand the innovative method that is introduced in the paper. It is highly Accessible, because all of the components are well designed and interlinked with the other sections of the paper and has a well defined Professional Appearance. The Correctness of the subject matter is one of the biggest advantages of this section because it is very difficult to correctly explain the definition of three interlinked matrices. The Accuracy of the material is very intense and it is defined with high Clarity.


The only major concern in this section is the Honesty of the material discussed. If we refer to the conclusions of theorem 1, then it challenges the honesty of declaring the K matrix in the section. Also the matter can be expressed in a more Comprehensive manner by removing the unwanted X sub variables that are discussed, which would also make the theory Concise.

Neural Network based model inversion control


After learning about the aided observer design it is very obvious to discuss the problems that will arise with the assertation of the theorems in the section and how the inversion control will be facilitated by the neural networks. The following section on neural network based model inversion control explains all these theorems and helps a reader to get a proper understanding of some of the regular problems that arise in day-to-day operational usage with the traditional models. The drawback of this section is that it resricts the attention to the case where the state variable and the control are of the same dimensions. This will put a hold on theorem one, which was discussed in the earlier section because it will not be able to explain the linearizing concepts. All the errors are approximated by assigning them a bigger database and representing observation control. The structure of this section is also very well defined because it advances with one assertation at a time. Towards the end of this section the reader would have a clear understanding of the base model and its design and the different problems that arise with it.


This section has been explained with utmost Honesty when it defines the functions of neural networks as controllers. It is highly Accurate and Accessible and compliant with the other sections of the paper. The equations and variables defined in this section give it an excellent Professional Appearance. Also the Correctness of the theories in this section helps the reader clearly understand the concept.


This section is unneccesarily strecthed in explaining and defining the variables which reduces the Comprehensiveness of the paper. The Conciseness of the paper is also affected by expanding the explanation of the equations and variables. Also the Clarity of the section is blurred by the introduction of the positive definite design matrix, which needs to be explained in a little more detail for clear understanding.

Simulation results


After we have the design of the model in our view, the only question remains is how will the results be output on the screen. To illustrate the performance of the observer model and the overall adaptive ouput feedback system. In this section the results are converted into graphical charts which makes them highly easy to understand and implement. It gives a pictorial presentation of the tracking performance in position and velocity. For instance this is demonstrated in figures , , 4, 5, 6, 7, 8. This gives the reader a thorough understanding of what he should expect after implementing the system. A research paper is not considered complete until it gives a satisfactory proof of all the assertations and theorems and it has assumed in the building of the system.


The simulation results have been Honestly reported even though some of them do not support the theory in the previous sections. The Clarity of this section is very good in displaying the results of simulations. The results are also defined in a very Comprehensive manner. The Accessibility of the graphs of the simulation are also very well defined and represented in a Concise manner. The Correctness of the graphs are also very well defined.


Since the results are honestly described in the section it affects the Accuracy of the whole theory. The only real drawback of this section is that it does not have a Professional Appearance because the graphs could be all combined into a simulation and represented as a matrix to keep it uniformed with the other sections of the paper.



Thus the final section of the paper namely the appendix, deals with the explaining of the previous theories that were assumed while building the model and removing the errors. The theorems are explained in great details and each step is carefully scritinized for each and every minor details that it posesses. For instance this is proved in the Pictorial reference of theorem 1 by specifying the figure of the complex vectorial displacements into coordinated sets. Also each of the mathematical equation is quite carefully handled.


The theories have been defined with high Clarity, Accuracy and Comprehensiveness. They are highly Accessible with the other sections of the paper and are related to all the assertations made earlier. The theorems give the paper a good Professional Experience and also confirms the Correctness of all the assumptions made earlier.


We had pointed out earlier in the Simulations section that Honesty of the subject matter conflicted with the proof of the theorems in the appendix. Even though the clarity, accuracy and comprehensiveness of the section is good the Conciseness could be made even more effective if unneccessary explanations of the design matrix would have been removed.

Overall Analysis of the Paper

Hence we see that overall the paper is quite well organized and presents logical sequential flow of events in it. The only drawback in the paper is that it tries to investigate each problem in great detail, which might sometimes cause confusion to the readers. For instance in the proof of theorem , the alpha function is analyzed in great details which is not actually required for proving the theory. Let us see how it matches to the different aspects of our evaluation

• Honesty Overall the honesty of the paper is well maintained throughout except for a couple of instances when the authors have tried to misled and tune in the simulation results in a way to match them with the theories. However these alterations are acceptable under normal operating circumstances of the model hence it does not affect the content or effectiveness of the paper.

• Clarity The clarity of the paper has been dealt with extreme effeciency and each and every aspect of the paper is defined clearly. From the basic definition of the variables to the implementation of the matrices all have been explained with precise clarity. Also the results have been displayed individually in the simulations section which makes it very clear to understand.

• Accuracy The accuracy of the paper is questionable in some aspects when it defines the simulations results and positive defining matrix in different sections which produce concluding results. However the authors have tuned them by defining a psuedo-control element, which handles the error approximation. But still overall the accuracy of the results could be questionable under some circumstances where extreme boundary conditions are applied.

• Comprehensiveness The paper is highly comprehensive and explains each and every theory that it uses. It would be very unlikely that the readers require any knowledge from other references to understand the paper. It is highly self contained and complete and explains the whole concept effectively and effeciently.

• Accessibility The accessibility of the paper is is very good and each section is related to the previous and next ones. Especially the relationship between the aided observer and implementation of the model demonstrates a unique combination. Overall each section is highly combined and accessible and gives the reader a complete understanding of the subject.

• Conciseness This is one quality that the authors need to improvise on the paper. The theorems and the simulation results have been unneccessarily stretched and too many variables have been introduced. For instance the positive definition matrix has ten sub variables, where if evaluated properly, only six are required.

• Professional Appearance This is one part where this paper has excelled above many other research papers that are published these days. The representation of the paper has been in a highly intellectual manner and gives it a good professional look.

• Correctness This is an element which is questionable in some sections of the paper. In the aided observer section some of the theorems are not used correctly in correllation model design and vice versa. But overall the concept explained in the paper is correct and could be of good use if the model is implemented into practical use.


Overall this project sounds like an excellent breakthrough in the research of neural dynamics and if successfully implemented would have great uses in the coming future. It could bring a breakthrough in navigation systems applied in airlines or automobiles or in other industries. We have evaluated and listed the downsides and advantages of each section and overall impact of the paper and if these are handled properly the paper would be a boon to the research advances in the field of neural networks and artificial intelligence.

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