Artificial Neural Network

Artificial  Neural Network -   Seminar Topic

Artificial  Neural Network  (ANN)  is  gaining prominence  in  various applications  like  pattern  recognition,  weather prediction, handwriting recognition, face recognition, autopilot, robotics, etc. In electrical engineering, ANN is being extensively researched in load forecasting, processing substation alarms and predicting weather for solar radiation and wind  farms.  With more focus on smart grids, ANN has an important  role. ANN belongs to the family  of Artificial Intelligence along with Fuzzy Logic, Expert Systems, Support Vector Machines. This paper gives an introduction into ANN and the way it is used.

Artificial Neural network is a system loosely modeled on the human brain. The field goes by many names, such as connectionism; parallel distributed processing, euro computing, natural intelligent systems, machine learning algorithms and artificial neural networks. 

It is an attempt to simulate within specialized hardware or sophisticated software, the multiple layers of simple processing elements called neurons. Each neuron is linked to certain of its neighbors with varying coefficients of connectivity that represent the strengths of these connections. 

Learning is accomplished by adjusting these strengths to cause the overall network to output appropriate results.

Artificial Neural Network History

Brief History of Neural Networks - medium.com

What’s in Store for the Future? Neural Network

With all those strengths fueling the future of neural nets and all those weaknesses complicating things

Integration. The weaknesses of neural nets could easily be compensated if we could integrate them with a complementary technology, like symbolic functions. The hard part would be finding a way to have these systems work together to produce a common result—and engineers are already working on it.

Sheer complexity. Everything has the potential to be scaled up in terms of power and complexity. With technological advancements, we can make CPUs and GPUs cheaper and/or faster, enabling the production of bigger, more efficient algorithms. We can also design neural nets capable of processing more data, or processing data faster, so it may learn to recognize patterns with just 1,000 examples, instead of 10,000. Unfortunately, there may be an upper limit to how advanced we can get in these areas—but we haven’t reached that limit yet, so we’ll likely strive for it in the near future.

New applications. Rather than advancing vertically, in terms of faster processing power and more sheer complexity, neural nets could (and likely will) also expand horizontally, being applied to more diverse applications. Hundreds of industries could feasibly use neural nets to operate more efficiently, target new audiences, develop new products, or improve consumer safety—yet it’s criminally underutilized. Wider acceptance, wider availability, and more creativity from engineers and marketers have the potential to apply neural nets to more applications.

Obsolescence. Technological optimists have enjoyed professing the glorious future of neural nets, but they may not be the dominant form of AI or complex problem solving for much longer. Several years from now, the hard limits and key weaknesses of neural nets may stop them from being pursued. Instead, developers and consumers may gravitate toward some new approach—provided one becomes accessible enough, with enough potential to make it a worthy successor.

THE ANALOGY TO BRAIN

The most basic components of neural networks are modeled after the structure of the brain. Some neural network structures are not closely to that of the brain and some does not have a biological counterpart in the brain. However, neural networks have a strong similarity to the biological brain and therefore a great deal of the terminology is borrowed from neuroscience.

Sources / References:

https://en.wikipedia.org/wiki/Artificial_neural_network

https://www.researchgate.net/publication/319903816_AN_INTRODUCTION_TO_ARTIFICIAL_NEURAL_NETWORK

https://readwrite.com/2019/01/25/everything-you-need-to-know-about-the-future-of-neural-networks/

https://medium.com/analytics-vidhya/brief-history-of-neural-networks-44c2bf72eec