Questions about Neural Networks
Q1. What are Neural Networks? What are the types of Neural networks?
Answer: In simple words, a neural network is a connection of many very tiny processing elements called as neurons. There are two types of neural network-
Biological Neural Networks– These are made of real neurons.Those tiny CPU’s which you have got inside your brain..if u have..Not only brain,,but neurons actually make the whole nervous system.
Artificial Neural Networks– Artificial Neural Networks is an imitation of Biological Neural Networks,,by artificial designing small processing elements, in lieu of using digital computing systems that have only the binary digits. The Artificial Neural Networks are basically designed to make robots give the human quality efficiency to the work.
Q2. Why use Artificial Neural Networks? What are its advantages?
Answer: Mainly, Artificial Neural Networks OR Artificial Intelligence is designed to give robots human quality thinking. So that machines can decide “What if” and ”What if not” with precision. Some of the other advantages are:-
- Adaptive learning: Ability to learn how to do tasks based on the data given for training or initial experience.
- Self-Organization: An Artificial Neural Networks can create its own organization or representation of the information it receives during learning time.
- Real Time Operation: Artificial Neural Networks computations may be carried out in parallel, and special hardware devices are being designed and manufactured which take advantage of this capability.
- Fault Tolerance via Redundant Information Coding: Partial destruction of a network leads to the corresponding degradation of performance. However, some network capabilities may be retained even with major network damage.
Q3. How are Artificial Neural Networks different from Normal Computers?
Answer: Simple difference is that the Artificial Neural Networks can learn by examples contrary to Normal Computers who perform the task on Algorithms. Although, the examples given to Artificial Neural Networks should be carefully chosen. Once properly “taught” Artificial Neural Networks can do on their own,,,or at least try to imitate..But that makes them so Unpredictable , which is opposite to that of algorithm based computers which we use in our daily life.
Q4. How human brain works?
Answer: It is weird at the same time amazing to know that we really do not know how we think. Biologically, neurons in human brain receive signals from host of fine structures called as dendrites. The neuron sends out spikes of electrical activity through a long, thin stand known as an axon, which splits into thousands of branches. At the end of each branch, a structure called a synapse converts the activity from the axon into electrical effects that inhibit or excite activity from the axon into electrical effects that inhibit or excite activity in the connected neurons. When a neuron receives excitation input that is sufficiently large compared with its inhibitory input, it sends a spike of electrical activity down its axon. Learning occurs by changing the effectiveness of the synapses so that the influence of one neuron on another changes.
Q5. What is simple Artificial Neuron?
Answer: It is simply a processor with many inputs and one output….It works in either the Training Mode or Using Mode. In the training mode, the neuron can be trained to fire (or not), for particular input patterns. In the using mode, when a taught input pattern is detected at the input, its associated output becomes the current output. If the input pattern does not belong in the taught list of input patterns, the firing rule is used to determine whether to fire or not.
An Artificial Neuron
Q6. How Artificial Neurons learns?
Answer: This is a two paradigm process-
- Associative Mapping: Here the network produces a pattern output by working in a pattern on the given input.
- Regularity Detection: In this, units learn to respond to particular properties of the input patterns. Whereas in associative mapping the network stores the relationships among patterns, in regularity detection the response of each unit has a particular ‘meaning’. This type of learning mechanism is essential for feature discovery and knowledge representation.
Q7. List some commercial practical applications of Artificial Neural Networks.
Answer: Since neural networks are best at identifying patterns or trends in data, they are well suited for prediction or forecasting needs including:
- sales forecasting
- industrial process control
- customer research
- data validation
- risk management
- target marketing
Q8. Are Neural Networks helpful in Medicine?
Answer: Yes of course…
(1) Electronic noses
ANNs are used experimentally to implement electronic noses. Electronic noses have several potential applications in telemedicine. Telemedicine is the practice of medicine over long distances via a communication link. The electronic nose would identify odors in the remote surgical environment. These identified odors would then be electronically transmitted to another site where an door generation system would recreate them. Because the sense of smell can be an important sense to the surgeon, telesmell would enhance telepresent surgery.
(2) Instant Physician
An application developed in the mid-1980s called the “instant physician” trained an auto-associative memory neural network to store a large number of medical records, each of which includes information on symptoms, diagnosis, and treatment for a particular case. After training, the net can be presented with input consisting of a set of symptoms; it will then find the full stored pattern that represents the “best” diagnosis and treatment.
Q9. What are the disadvantages of Artificial Neural Networks?
Answer: The major disadvantage is that they require large diversity of training for working in a real environment. Moreover, they are not strong enough to work in the real world.
Q10. How Artificial Neural Networks can be applied in future?
(1) Pen PC’s: PC’s where one can write on a tablet, and the writing will be recognized and translated into (ASCII) text.
(2) White goods and toys: As Neural Network chips become available, the possibility of simple cheap systems which have learned to recognize simple entities (e.g. walls looming, or simple commands like Go, or Stop), may lead to their incorporation in toys and washing machines etc. Already the Japanese are using a related technology, fuzzy logic, in this way. There is considerable interest in the combination of fuzzy and neural technologies.