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The purpose of this paper is to analyze how pattern recognition can contribute to the behavioral options of a goal‐oriented system.

A functional approach is used to develop the necessary cybernetic structures of a pattern recognition unit that can store observations as new standards for pattern matching by itself and can later apply them to recognize patterns in incoming sensor data.

Combining such a structure for pattern recognition with a feedback system shows that the resulting system can only deal with known patterns. To deal with novel patterns this structure has to be added to an adaptive system that can develop system‐specific behavior. Such a system has to able to initiate a trial and error process to test new behavior towards new patterns and to evaluate its effect on the highest, existential goal‐values of the system.

A system with a pattern recognition unit that can set new standards for pattern matching by itself is identified as the point of departure where not‐programmable and unpredictable individual behavior starts. Dealing with newly‐recognized pattern requires individual behavioral solutions and a system‐specific evaluation of the achieved results in relation to the highest goal‐values of the system. Here internal “emotional” criteria to select behavior emerge as a cybernetic necessity.

The paper is the third in a series of three on a cybernetic theory distinguishing system capable of pre‐programmed adaptation, system‐specific adaptation and learning. It determines the cybernetic starting point of individual psychology.

The concept of a fuzzy language is applied to pattern recognition using geometric figures, chromosomes and leukocytes as illustrative examples. For chromosomes, an algorithm for classifying a chromosome image as an “approximate median chromosome,” “approximate sub‐median chromosome” or “approximate acrocentric chromosome” is presented. For leukocytes, an equal‐perimeter circular shape measure and an equal‐area circular shape measure are proposed, and various properties, results, and the relationship between these two measures are presented. Quantitative measures of other visual concepts such as elongated, spiculed, indented, slightly indented and deeply indented arc also presented and illustrated by examples. The results obtained in this paper may have useful applications in pattern recognition, cybernetics and fuzzy systems.

The purpose of this paper is to analyze how sequence learning can build on pattern‐recognition systems and how it can contribute to the behavioral options of goal‐oriented systems.

A functional approach is used to develop the necessary cybernetic structures of a subsystem for sequence learning, that can recognize patterns, register patterns occurring repeatedly and connect these to sequences. Based on that it is analyzed how goal‐oriented systems can use information about reoccurring sequences.

A subsystem for sequence learning basically requires pattern recognition and it needs a structure for the directed connection of single standards for pattern matching to standards for sequences, given that it can learn both new patterns and new sequences. Such a subsystem for sequence learning may recognize a certain pattern and with that the end of a certain sequence. So it may deliver more than one output signal at a point in time, and therefore needs additionally a subsystem for directing attention.

The paper analyses the principles of an “associative” way of connecting standards for pattern matching to standards for sequences. Also it shows the cybernetic necessity of an attention directing system that has to decide how to deal with the multiple outputs of a subsystem for sequence learning, i.e. to decide to act either towards a pattern or a whole sequence.

The paper investigates basic mechanisms of sequence learning and its contribution to goal‐oriented behavior. Also, it lays the base for an analysis of attention directing systems and anticipatory systems.

The electronic nose is an array of chemical or gas sensors and associated with a pattern-recognition framework competent in identifying and classifying odorant or non-odorant and simple or complex gases. Despite more than 30 years of research, the robust e-nose device is still limited. Most of the challenges towards reliable e-nose devices are associated with the non-stationary environment and non-stationary sensor behaviour. Data distribution of sensor array response evolves with time, referred to as non-stationarity. The purpose of this paper is to provide a comprehensive introduction to challenges related to non-stationarity in e-nose design and to review the existing literature from an application, system and algorithm perspective to provide an integrated and practical view.

The authors discuss the non-stationary data in general and the challenges related to the non-stationarity environment in e-nose design or non-stationary sensor behaviour. The challenges are categorised and discussed with the perspective of learning with data obtained from the sensor systems. Later, the e-nose technology is reviewed with the system, application and algorithmic point of view to discuss the current status.

The discussed challenges in e-nose design will be beneficial for researchers, as well as practitioners as it presents a comprehensive view on multiple aspects of non-stationary learning, system, algorithms and applications for e-nose. The paper presents a review of the pattern-recognition techniques, public data sets that are commonly referred to as olfactory research. Generic techniques for learning in the non-stationary environment are also presented. The authors discuss the future direction of research and major open problems related to handling non-stationarity in e-nose design.

The authors first time review the existing literature related to learning with e-nose in a non-stationary environment and existing generic pattern-recognition algorithms for learning in the non-stationary environment to bridge the gap between these two. The authors also present details of publicly available sensor array data sets, which will benefit the upcoming researchers in this field. The authors further emphasise several open problems and future directions, which should be considered to provide efficient solutions that can handle non-stationarity to make e-nose the next everyday device.

The purpose of this paper is to develop a control chart pattern recognition methodology for monitoring the weekly customer complaints of outsourced information technology-enabled service (ITeS) processes.

A two-step methodology is used to classify the processes as having natural or unnatural variation based on past 20 weeks' customer complaints. The step one is to simulate data on various control chart patterns namely natural variation, upward shift, upward trend, etc. Then a deep learning neural network model consisting of two dense layers is developed to classify the patterns as of natural or unnatural variation.

The validation of the methodology on telecom vertical processes has correctly detected unnatural variations in two terminated processes. The implementation of the methodology on banking and financial vertical processes has detected unnatural variation in one of the processes. This helped the company management to take remedial actions, renegotiate the deal and get it renewed for another period.

This study provides valuable information on controlling information technology-enabled processes using pattern recognition methodology. The methodology gives a lot of flexibility to managers to monitor multiple processes collectively and avoids the manual plotting and interpretation of control charts.

The application of control chart pattern recognition methodology for monitoring service industry processes are rare. This is an application of the methodology for controlling information technology-enabled processes. This study also demonstrates the usefulness of deep learning techniques for process control.

The purpose of this paper is to analyze how elementary anticipation, understood as anticipation of the repetition of one known pattern, can emerge out of sequence learning and how it can contribute to the behavioral options of goal‐oriented systems.

A functional approach is used to develop the necessary cybernetic structures of a subsystem for sequence learning that can additionally provide standards of anticipated patterns for future pattern matching. Based on that it is analyzed, how a goal‐oriented system can use the information about the actual occurrence of an anticipated pattern.

A subsystem for elementary anticipation of single patterns builds on sequence learning and requires additionally a structure: first, to unequivocally identify the beginning of known sequences just from their first patterns; and second, to decide to use a latter pattern of such a sequence as standard for an anticipated pattern. Deciding to actually use such a pattern for anticipation requires an additional subsystem to switch between the feedback pattern recognition mode and feedforward. Then the occurrence of such an anticipated pattern allows immediate recognition and action.

The paper shows a necessary evolution of cybernetic structures from pattern recognition via sequence learning to anticipation; and it shows, too, a necessary evolution in the cognitive development of individual systems. In the simple anticipatory structures analyzed here, only known patterns, that are part of a known sequence, can become anticipated patterns.

The paper places elementary anticipation of single patterns in an evolutionary development based on pattern recognition and sequence learning. It provides the base to analyze more complex forms of anticipation.

The purpose of the paper is to analyze cybernetic necessities of output‐side attention directing systems, i.e. how systems can decide to act towards one of various inputs.

Complex pattern recognition and sequence learning systems may recognize more than one pattern and deliver more than one output at a point in time. Therefore, they require an output‐side attention directing system to decide to act towards just one pattern. The necessary cybernetic structures of such systems are analyzed using a functional approach.

An output‐side attention directing system has to evaluate the effect of current observations (patterns, sequences, etc.) on highest level goal‐values (in a living system these are existential goal‐values like a body temperature or energy supply). Measure of this effect is the degree of goal‐approximation towards these goal‐values. This measure can either be preprogrammed for some patterns or sequences, or has to be determined in trial and error processes for new patterns or sequences learned by the system.

The paper shows the cybernetic necessities of the development of the “know how” of sequence learning systems in time, starting with default behavior, via learning new patterns and sequences, and trial and error to develop goal‐orientated actions towards them, until finally the achieved results enable experience based directing of attention.

The paper shows basic cybernetic structures and functions for output‐side attention directing systems required for all complex pattern recognition and sequence learning systems.

A purely theoretical approach has been found to be of limited value in the solution of practical Pattern Recognition problems. Difficulties arise when relating infinite mathematics to reality, e.g. “algorithmic convergence” must be replaced by a vaguer notion of “satisfactory performance”. Experimentation has been used to study this and related problems: a) Learning in noise; b) Similarity of classifiers; c) Instability of classifiers; d) Relating infinite‐sample analysis to finite data sets (reference to pdf estimation). Finally, the system requirements for effective experimentation are discussed.

Pattern recognition applied to control charts centers around the development and assessment of automated algorithms for detecting non‐random or unnatural patterns in observations collected from a production process. The work presented here marks the first examination of enhancements to an existing algorithm, of investigations into sensitivity analysis issues, of development of standard performance metrics, and of a comparative performance with the traditional Western Electric Run tests. The simulation results of the research presented here indicate that the modified algorithm performs markedly better than the original algorithm, is only slightly sensitive to the selection of the user specified algorithm parameters, and competes favorably with the Western Electric Run Tests especially when detecting repetitive patterns like cycles.

The purpose of this paper is to investigate whether visionary management be learned.

The author, an experienced futurist, asks and answers the question, “Can visionary management be learned?“

The paper finds that new research suggests that managers can develop skills associated with successful visionaries.

One particularly important aspect of visionary management is the use of intuition, which experts describe as unconscious pattern recognition. The pattern recognition of trends for futurists and visionaries needs to be based on high quality information and disciplined imagination. An excellent approach to pattern recognition is the use of scenarios for anticipating and planning for the future. Scenarios prepared according to best practices share certain characteristics that provide high quality information and disciplined imagination.

The scenario method certainly can be learned by managers to the extent that they include information and pattern recognition within a prescribed rigor, scenarios are a viable way to teach intuitional skills to managers. These skills of intuition expand the manager's capability to become visionary.