SCINET • WORLD TRADE SYSTEM ® Automatic Commercial Operations • Import-Export • On-line Payment Systems • More than 20 million classified companies in 245 countries • 60 million goods, products and services • Production Mini-plants in mobile containers SCiNet Associates 24Hours consulting service PERCEPTUAL® ISTP | SCINET OPERATING SYSTEM The ISTP Perceptual Operating System is based on a combination of high technology applied programs. Self-replicating structures and non-linear, dynamic systems programs, capable of mutation and recombination, using mathematical models, neural networks, semantic and heuristic networks, Boolean operators, expert systems, decision-making procedures, axiomatic semantics, increased transmission networks, deductive reasoning, numerical factors, space factors, variations, permutations, combinations, random events, probability, etc., on automated, logical decision-making through exametric criteria (where, how, when, what, who, and why), interaction and iteration, of a magnitude of 10 Teraflops (i.e., 10 trillion bits per second.) SCiNet / RSi consists of a select group of researchers, scientists and specialists in International Regulations and Trade, Technological Systems, IT Systems, Telecommunications, Industrial Processes, Engineering, Security, Artificial Intelligence, Intelligent System Languages, Neural Networks, Robotic Intelligence, Parallel Algorithms, Nanotechnology and Neurocomputers, Protein-Based Computers, Quantum Computers, Process Control in Real Time, Encrypted Systems, and others.Most of these groups of collaborators reside in various cities and countries while remaining in permanent and mutual communication. Each group is in charge of the research, development and control of specific processes. In turn, each group is reciprocally linked to other SCiNet groups, centers and operating units, thus forming the RSi Network and the CT1 Virtual Applied Technology Centers. CONCEPTUAL STRUCTURE AND TECHNOLOGICAL COMPONENTS 0012-RN. Neural Network Neural networks are a form of calculations and computations inspired by brain structure and intended to emulate its functioning. The topology is a weighted, directed graph. The nodes can be connected or disconnected. Time is considered to be discrete. At each moment all connected nodes send an impulse along their output arcs to their neighboring nodes. All nodes sum up their incoming impulses, which are weighted according to the arc. All nodes in which this addition exceeds a certain threshold turn “on” at the next instant, while the others are turned “off.” The calculations take place by setting some input nodes to “waiting” for the network to reach a certain state, and then reading some exit nodes. Nodes may be set up, by using syllogisms, to recognize certain structures, such as classifying objects based on their features. 0016-RS. Semantic Network A semantic network is a relational knowledge representation medium in the form of a weighted, labeled graph. Each vertex of the graph represents a concept and each label a link between concepts. Accessing and updating procedures cross and manipulate the graph. A semantic network is considered, at times, as a graphic notation for logical formulae. Semantics is construed as part of the definition of a language that specifies the meaning or the effect of a text that is constructed by following the syntactic rules of the language. 0022-BS. Knowledge Base Qualitative compilation of knowledge selected over a particular domain that has been formalized in the right presentation to be able to perform reasoning. It is found in the context of expert systems, where the knowledge base can present the rules and experiences in that domain (for example, medicine or electronics). Normally the knowledge is expressed in a format of production rules and is represented by the heuristic approach that the knowledge base has developed by applying formal knowledge in the course of solving problems. Other forms of representing knowledge are logical formulas, semantic networks and information or representation units. Within expert systems there are two important classes of knowledge bases, the static and the dynamic. A static knowledge base has the domain knowledge needed to carry out the solution of problems. The dynamic knowledge base is used for storing pertinent information for solving a particular problem. 0026-VL. Logical Value A logical value is either of two values, a true value or a false value, that indicates a real value. Although a single binary digit (bit) is the clearest structure of computer storage that can be applied to logical data, in practice, larger storage units are frequently used, such as an information unit (byte), since they can be addressed in a different manner. 0036-AL. Logical Analyzer A logical analyzer is an electronic instrument that manages the logical states of digital systems and stores the results for subsequent visual display. The storage of data begins at the analyzer with the recognition of the activation status established beforehand, as it appears in the system being tested. Synchronic analyzers show data at intervals determined entirely by the external system. Asynchronous analyzers carry out this task at intervals determined internally through the analyzer. The essence of a logical analyzer resides in the fact that it operates with many parallel channels (frequently 8, 16 or 32), and in the fact that the data recorded can be read from the memory at will, either in binary format or after decoding, often by means of a scrambler. 0042-LCD. Fuzzy Set Logic Logical Fuzzy Set Theory; Polyvalued Logic Theory. It is a branch of logic designed specifically to represent knowledge and human reasoning such that it can be processed by computer. This theory is applicable to Expert Systems, Knowledge Engineering, and Artificial Intelligence. The more traditional predicate and propositional logic does not allow degrees of uncertainty, as indicated by words or phrases such as quite, very, many, very likely. Instead of true values such as true and false, it is possible to introduce a multiple value logic, i.e., with a set of values, that will understand, for example, values such as true, untrue, very true, not very true, more or less true, not very false, very false, not false and false. Alternatively, an interval such as [0,1] can be introduced and the degree of truth can be represented by a real number in this scale. Predicates are functions that do not result in {true, false} but in these other more general domains. The polyvalued logic theory is responsible for the study of sets and predicates in this class. Thus arises the concept of polyvalued sets, polyvalued relations and polyvalued quantifiers. 0044-OP. Boolean Operator Formally, a Boolean operator is a complemented and distributive network or reticulated system (George Boole, Laws of Thought Theory). In Boolean mathematics, there is a set of elements B that consists of only 1’s and 0’s. Likewise there are two dyadic operations (closely linked to each other), which as a general rule are identified by the symbols Ù and Ú (or by. and +) and are called respectively “y” and “o”. In addition, there is a monadic operation (indivisible, but of a different nature), indicated here by the symbol ' and known as the complementing operation. Boolean Expression (Logical Expression). Expression of Boolean algebra, that is, a well-formed formula of Boolean constants and variants linked by Boolean operators. A combinatorial circuit can be formed fully and directly by means of a Boolean expression, but the same does not happen with sequential circuits (idempotent laws, association laws, commutative laws, absorption laws, distribution laws, identity laws, 1/0 laws, and complementarity laws). 0056-LP. Predicate Logic Calculation of Predicates: Predicate Logic, First Order Logic. A fundamental notation to represent and reason with logical sentences. It boosts propositional calculation by introducing quantifiers and allowing predicates and functions with any number of arguments. The syntax includes terms, atoms and formulas. An atom (or atomic formula) adopts the form P (t!...t) where P is a predicate symbol and t!, ... t: are terms. Formulas can be formed from these atoms in the following way: (i) an atom is a formula, (ii) formulas can combine through the connectives or the usual logical propositional operators (negation, conjunction, disjunction, etc.), (iii) if F is a formula, "v.F and $v are also formulas. A sentence is a formula without free variables. SCINET CORPORATION SCiNet Corporation • Science Network | The World Trade System SCiNet International Office | 24 Hour support service Info and Questions | Phone: (+34) 91-476-7000 | Fax: (+34) 91-388-3203 © 2008 SCINET CORPORATION. All rights reserved Legal Information. Do not duplicate or redistribute in any form