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INTELLIGENT SYSTEMS > M2.2 > Flashcards

M2.2 Flashcards

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1
Q

Three possible practical implementations of ANNs are:
1. A software simulation program running on a digital computer.
2. A hardware emulator connected to a host computer - the so-called _____.
3. A true electronic neural network

A

neurocomputer

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2
Q

This is currently the cheapest and fastest implementation method for ANNs

A

Software Simulations

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3
Q

Software Simulations simulates parallel processing on a conventional ___ digital computer

A

sequential

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4
Q

Software Simulations replicates ___behaviour of the network by updating the activation level and output of each node for successive time steps

A

temporal

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5
Q

Typical additional features of ANN simulators
1. Configuring the net according to a chosen architecture and node operational characteristic
2. Implementation of ___ phase using a chosen training algorithm

A

training

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6
Q

ANN simulators are written in ___ languages such as C, C++ and Java

A

hi-level

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7
Q

Main attraction of ANN simulators is the relatively low cost and wide availability of ready made commercial ___

A

packages

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8
Q

ANN simulators are also compact, flexible and highly ___

A

portable

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9
Q

Training of ANNs using software simulators can be slow for larger networks (greater than a few ___)

A

hundred

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10
Q

Commercially Available Neural Net Packages have prewritten ___ with convenient user interfaces

A

shells

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11
Q

Commercially Available Neural Net Package cost a few ___ of dollars

A

hundred to tens of thousands

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12
Q

Commercially Available Neural Net Package allow users to specify the ANN design and training ____

A

parameters

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13
Q

Commercially Available Neural Net Package usually provide ___ to enable monitoring of the net’s training and operation

A

graphic interfaces

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14
Q

Commercially Available Neural Net Package are likely to provide interfacing with other software systems such as ___ and databases

A

spreadsheets

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15
Q

___ are dedicated special-purpose digital computer (aka accelerator boards

A

neurocomputer

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16
Q

Neurocomputers are dedicated special-purpose digital computer (aka ___)

A

accelerator boards

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17
Q

Neurocomputers acts as a ___to a host computer and is controlled by a program running on the host

A

coprocessor

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18
Q

Neurocomputers can be tens to ___ of times faster than simulators

A

thousands

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19
Q

Neurocomputers systems are available with over 10 million ___ for networks with several million neurons

A

IPS

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20
Q

In neurocomputers, IPS means?

A

IPS (Interconnect updates Per Second)

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21
Q

___ in hardware are closer to biological neural networks

A

True Networks

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22
Q

True Networks in Hardware are consist of synthetic neurons actually fabricated on ___ chips

A

silicon

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23
Q

Commercially available ___ ANNs are limited to a few thousand neurons per chip

A

hardwired

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24
Q

The problem with true networks in hardware wherein it is hard to make all the neurons communicate properly across chips

A

Interconnection and interference issues

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25
The problem with true networks in hardware wherein some chips can’t change their "memory strength" after being made
fixed-valued weights
26
The problem with true networks in hardware wherein scientists are still working on making the connections changeable or a modifiable ___
synapses
27
This aims to add structure and organization to ANN applications development for reducing cost, increasing accuracy, consistency, user confidence and friendliness
Neural Network Development Methodology
28
Neural Network Development Methodology are split into (4) phases:
The Concept Phase The Design Phase The Implementation phase The Maintenance Phase
29
In Neural Network Development Methodolog, this phase involves o Validating the proposed application o Selecting an appropriate neural paradigm.
The Concept Phase
30
One application area unsuitable for ANNs is ___ management eg, inventory, accounts, sales data analysis
resource
31
Selecting an ANN Paradigm is based on comparison of application requirements to capabilities of different paradigms as well as the ___ method that can be employed
training
32
This phase of Neural Network Development Methodology specifies initial values and conditions at the node, network and training levels
The Design Phase
33
In Node-Level Decision of the the Design Phase, this type of input is like a light switch: ON or OFF (0,1)
Binary
34
In Node-Level Decision of the the Design Phase, is like YES/NO with more balance (-1, +1)
Bipolar
35
In Node-Level Decision of the the Design Phase, this type of input has three options: low, neutral, high (-1, 0, +1)
Trivalent
36
In Node-Level Decision of the the Design Phase, these are the other two types of input (2)
Discrete, Continuous
37
In Node-Level Decision of the the Design Phase, what is this type of Transfer Function If input is big enough, turn ON; else, stay OFF
Step/Threshold
38
In Node-Level Decision of the the Design Phase, what is this type of Transfer Function Smooth curve output between 0 and 1
Sigmoid
39
In Node-Level Decision of the the Design Phase, what is this type of Transfer Function Smooth curve from -1 to +1
Tang (hyperbolic tangent)
40
In Node-Level Decision of the the Design Phase, what is this type of Transfer Function Precomputed answers to speed things up
Lookup Table
41
In Network-Level Decision of the the Design Phase, __ is described as how many inputs you give (like pixels of an image)
Input layer size
42
In Network-Level Decision of the the Design Phase, __ is described as how many categories or clusters you expect
Output layer size
43
In the Design Phase, this type of connectivity is when neurons only connect between layers (like in Multilayer Perceptron or MLP)
Interlayer Connectivity
44
In the Design Phase, this type of connectivity is when neurons also connect within the same layer (like in Hopfield networks)
Intralayer Connectivity
45
In the Design Phase, __ feedback is once the signal moves forward, it doesn’t go back (like in MLP)
no feedback
46
In the Design Phase, __ feedback is when outputs can be sent back into the system to help adjust behavior (like in Hopfield net)
with feedback
47
This parameter in the Design Phase is defined as how fast the network updates its weights
learning rate
48
This parameter in the Design Phase is defined as when should training stop? (e.g., after 1000 rounds, or when error is small enough)
stopping rule
49
This parameter in the Design Phase is defined as adding small random numbers cto help the network learn faster (like shaking things up to avoid getting stuck)
adding noise
50
What is the last step in the implementation step of Neural Network Development Methodology o Gathering the training set o Selecting the developing environment o Implementing the neural network o ___
Testing and debugging the network
51
In gathering training data in the implementation step, Increasing data amount increases ___ time but may help earlier convergence
training
52
In gathering training data in the implementation step, quality more important than quantity. True or false
True
53
In the implementation step, when preparing for data, it usually includesnormalization and possible ___
binarization
54
In the implementation step, selecting the __ environment is also included. Such as picking the right hardware and software aspects
Development Environment
55
In the implementation phase, the most popular platforms to consider as the development environment are workstations and high-end PC's (with ___ board option)
accelerator
56
In the implementation phase, when choosing the software, this type of software requires more expertise on part of the user but provides maximum flexibility
Custom-coded simulators
57
In the implementation phase, when choosing the software, this type of software are usually easy to use because of a more sophisticated interface
Commercial developement packages
58
This phase of Neural Network Development Methodology is consists of o placing the neural network in an operational environment with possible integration o periodic performance evaluation, and maintenance.
The Maintenance Phase
59
In the Maintenance Phase, when putting the Network to Work, this type of setup Works by itself, like a calculator
Stand-alone
60
In the Maintenance Phase, when putting the Network to Work, this type of setup Works with other systems across a network
Distributed
61
In the Maintenance Phase, when putting the Network to Work, this type of setup Prepares data for another system
Preprocessor
62
In the Maintenance Phase, when putting the Network to Work, this type of setup Processes the output from another system
Postprocessor
63
In the Maintenance Phase, when putting the Network to Work, this type of setup Built directly into another system (like a smart fridge or robot)
Embedded
64
In the Maintenance Phase, when putting the Network to Work, this type of connection to other systems is when ANN works alongside other systems but is not deeply connected. Examples: preprocessor, postprocessor, separate module
Loose-coupling
65
In the Maintenance Phase, when putting the Network to Work, this type of connection to other systems is when ANN is fully integrated into another system — it feels like part of the main program or machine
Tight-coupling
66
In the Maintenance Phase, the two main wais to AN maintenance is (2)
Data Maintenance Software Maintenance

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