1 Cybernetics Flashcards
2 Cybernetics
2 Cybernetics—the study of control and connections in nature, science, and society Basic concepts: Organization (systems theory) Information (information theory) Control (control theory)
3 Organization. Systems theory
3 Systems theory — the study of systems in general, with the goal of elucidating principles that can be applied to:
- all types of systems
- at all nesting levels
- in all fields of research.
4 Organization
4 Organization—formation of systems
Cybernetic system:
• interacting structures and processes combined
for the execution of a common function
• which function is different from functions of
the separate components
5 General Properties of Cybernetic systems
5 • Interact with the environment and with other
systems — connections
• Have hierarchical structure:
- consist of subsystems
- are subsystems of other systems
• Preserve their general structure in changing
environmental conditions
6 Cybernetic Systems
6 Can be characterized using three types of
functions describing the changes of system:
• component states
• structure and connections
• transmitted signals
7 Types Of Systems By The Degree Of Determinism Of Their Response
7 • Deterministic -
components interact in a predetermined way and response is predictable. Example: machine
• Probabilistic -
response can not be exactly predicted. Example: weather
8 Types Of Systems By The Type Of lnteraction With The Environment
8 Closed • the components interact with each other only • no interactions with the environment Open • the components interact with the environment as well
9 Elements Of The lnteraction
9 Perception of signals from other systems using sensors (receptors)
Examples: eyes, ears, etc.
Transmission of signals to other systems using effectors
Examples: organs of speech, gestures, etc
10 Biological Cybernetic Systems
10 Biological cybernetic systems characteristics • varying complexity • probabilistic • multi-level hierarchical organization Basic properties • self-organization • self-regulation
11 Biological Systems - Complexity
11 Very complex:
• large number of components
• complex and interrelated connections
between the components
12 Biological Systems - Determinism
12 Probabilistic: • large number of components • large number of connections between the components • strong external influences
13 Biological Systems - Organization
13 Complex two-way hierarchy - Each component can
be regarded as a system of lower-level components
• The low level components perform independently
of the higher level components as long as they are
able to process all the important input information
• The high level components control the lower level
components
14 Information
14 • Any set of related data
• Any meaningful event, which results in an
action
• The state of a system of interest
Information reduces ambiguity, removes the
lack of knowledge
15 lnformation Theory
15 Study of information: • acquisition • transmission • storing and retaining • processing • measuring
16 Communication System
16 [pic] the mathematical theory of communication:
info source-> message -> transmitter-> signal-> received signal-> receiver -> message -> destination
17 Messages, Signals, and Channels
17 • Message - the transmitted information
• Signal - the physical carrier of the message
• Communication channel- the medium in
which the signal propagates
Examples (signal - channel):
• sound wave - air
• light wave - optical fibre
• electric signal- wire in an electronic device
18 Alphabet (Code)
18 • Alphabet - a set of simple signals which can be
used to send any message
• Encoding (by transmitter) – generation (using
an alphabet) of a signal which carries the
message
• Recoding - altering the alphabet
• Decoding (by receiver) - extraction of the
message from the signal
19 Alphabet (Code) example
19 [pic]
20 Isomorphism And Noise
20 • lsomorphic signals - physically different signals
which carry the same message
- Recoding should ensure the initial and recoded signals are isomorphic
• Noise - communication system disturbances which modify the signal
- Channel fidelity indicator - the signal to
noise ratio (SNR)
21 Storing And Retaining lnformation
21 Memory- the ability of a system to store and
retain information, and to recall it for use at a
later moment
Ways to memorize information:
• by changing the states of system components
• by changing the structure of the system (the
connections between its components)
22 Measuring lnformation
22 lf an experiment is to produce any one of a set of N
equally likely events, the amount of information I received when we learn which event has occurred is:
I = 𝐥𝐨𝐠𝟐N
Unit of measurement: the bit
One bit is the amount of information received when we learn which one of 2 (two) equally likely events has occurred. Example: tossing a coin
23 Information in the human DNA
23 • DNA contains 4 bases. Any nucleotide contains only one base. Therefore, the information carried by one nucleotide is 2 bits.
• The chromosomal DNA of one human sperm contains 10^9 nucleotides, i.e. information of 2X10^9 bits.
24 Control
24 • Control — actions effecting a system and aimed at reaching a specific goal
• Regulation - control for maintaining a specific state or process
• Cybernetic Control System — One that is selfcontained
in its performance monitoring and correction capabilities
25 Program And Reference
25 • Program - the set of rules (algorithm) used to
control a system
• Reference—the law describing how the controlled system must behave
• The program and/or reference may be included in the control system itself or be received from another cybemetic system at a higher hierarchical level
26 Control System
26 Controlling subsystem – processes information, generates and sends control messages (commands)
• Controlled subsystem – changes according to the messages received
• Connections – communication subsystems transferring information between the controlling and controlled subsystems
27 Open-Loop Control
27 Controlling subsystem -> Controlled subsystem
• The execution of the control messages is not monitored
• Used if noise is missing and the properties of the controlled system do not change
->
Forward-coupling connection —transmits control messages from the controlling to the controlled subsystem
28 Closed-Loop Control
28 Controlling subsystem ->
29 Closed Loop Control System in the Body (Reflex Arc)
29 • Receptors Transform the stimulus into excitation • Afferent (sensory) neurons Back-coupling (feedback) channel • Neural centre Controlling subsystem (issues commands) • Efferent (motor) neurons Forward-coupling channel • Effectors Respond to the commands
30 Positive Feedback
30 Positive feedback (self-reinforcing loop) - the control results in increased divergence of the controlled subsystem.
Divergence - the difference between the current and preceding states of a system The controlled process accelerates until the limiting constraints of the controlled subsystem are reached.
31 Significance Of Positive Feedback Loops
31 Beneficial:
—amplify vital processes
—provide adaptation - fast response to external factors
and transition from the initial state to another, more appropriate state
Detrimental:
—aggravate morbid conditions
32 Beneficial Positive Feedback
32 Products of food digestion:
33 Detrimental Positive Feedback
33 Cardiac insufficiency reduces blood supply to the heart:
34 Stress and Positive Feedback
34 • A psychological event resulting in preoccupation with weight;
• Food avoidance leading to elevated cortisol levels mobilizing stored liver glycogen to increase blood glucose;
Resulting positive feedback loop (in the absence of timely medical intervention) promote adverse effects, even death.
35 Negative Feedback
35 • Negative feedback (self-correcting loop or balancing loop) - the control results in balancing of the controlled subsystem
Balancing - minimizing the difference between the controlled parameter and the reference (setpoint)
• Ensures the quality and reliability of the control system
36 Negative Feedback Regulation System
36 Determine the error ΔX of the actual value X relative to the setpoint Xo.
Generate a control message such as to reduce ΔX.
37 Significance Negative Feedback Loops
37 Ensure:
• stability of body functions
• constant values of vital parameters
• resistance to external factors
Basic mechanism of:
• Homeostasis (the stable condition inside the body)
• the balance of energy and metabolites in the body
• the control of the populations of species etc.
38 Negative Feedback - example
38 Regulation of body core temperature:
• Body temperature exceeds the setpoint:
—lntensity heat loss from the body – vasodilation sweating, flat lying skin hairs, etc.
—Reduce heat production - restricted movements, less food consumption, etc.
• Body temperature is below the setpoint:
—Reduce heat loss
—lntensify heat production - shivering, metabolic efficiency. etc
39 Types Of Control And Quality Of The Control System
39 Control area — area between the curves of the reference and actual values of the controlled parameter
40 Scientific Modelling
40 Model—a simplified physical or mathematical representation of a system used for its investigation
• Modelling—methods for investigation of systems
using their models
Types of models;
• mathematical
• physical
• biological
41 Scientific Modelling and Simulation
41 • The model represents the system itself, whereas the simulation represents the operation of the system over time.
• Computer simulation has become a useful part of modeling many natural systems in physics, chemistry and biology
• Medical simulators have been developed for training procedures ranging from the basics to laparoscopic surgery and trauma care, etc.
42 Mathematical model
42 • Mathematical description of some aspects of the real system.
• Uses mathematics and computers to produce information about the studied system.
Example: Regulation of blood glucose concentration.
43 Physical model
43 Material object performing similarly to the real system.
Example: Electrical circuit modeling transitional processes in a nerve fibre.
44 Biological model
44 Laboratory animal used to reproduce specific conditions of the human body.
Requires less simplifying assumptions than mathematical
and physical models.
Example: Investigation of infections, poisons, pharmaceuticals, etc.
