Rule-base Inference Methodology Research Articles

A new belief rule base inference methodology with interval information based on the interval evidential reasoning algorithm

A new belief rule base inference methodology with interval information based on the interval evidential reasoning algorithm

Convolutional rule inference network based on belief rule-based system using an evidential reasoning approach

With the development of artificial intelligence, we pay widespread attention to the high reliability and interpretability of resulting architectures and algorithms. As one of the main methods of machine learning, the “black-box” mechanism of neural networks lacks interpretability. In order to alleviate the low interpretability, based on belief rule-based inference methodology using the evidential reasoning approach (RIMER), we propose a convolutional rule inference network (CRIN), which is interpretable by involving rules in belief rule base (BRB) under various types of uncertain knowledge representation. Considering the influence of global data distribution on attribute weights, the determination of each attribute may be affected by all other attributes, based on the sigmoid activation function, we determine optimized attribute weights to make sure that each attribute weight is formed by all attribute values. The derivation functions with optimized attribute weights based on evidential reasoning theory also showed to ensure the feasibility of the following learning algorithm. For expressing the influence of local rules input on final output in the network, the network framework and learning algorithm of CRIN based on convolution strategy are proposed. The inference process in RIMER is used as the feedforward mechanism to ensure the interpretability of the network. Meanwhile, the gradient descent method is used as back propagation algorithm to adjust the parameters of rule base to establish a more reasonable BRB. The experimental results and comparative analysis demonstrate that the proposed CRIN has advantages in terms of interpretability and learning capability. This study suggests new trends for interpretable network and uncertain classification issues in the reasoning approach.

A method of automatically generating initial parameters for large-scale belief rule base

The traditional rule-base inference methodology using evidential reasoning approach (RIMER) needs to traverse the reference values of all antecedent attributes when constructing belief rule base (BRB). Therefore, when many attributes have multiple reference levels, the scale of BRB will face the problem of combination explosion. Thus, it is inoperable to require experts to give all the parameters of each rule of BRB model, because of the limitation of expert knowledge in complex problems. If the initial BRB parameters are set unreasonably, the optimization speed and accuracy of the model will be affected. To solve this problem, a method of automatically generating initial parameters for large-scale BRB by using part of the standard rules and cloud model is proposed in this paper. Experts determine the number and parameters of standard rules through prior knowledge based on specific practical problems, and use cloud models to convert qualitative knowledge and quantitative information to automatically generate the remaining rules. A case study is established with Intel Berkeley Research lab data set to verify the effectiveness of the proposed method.

Effectiveness Evaluation of Aircraft Electromagnetic Launch System Based on RIMER

Electromagnetic launching technology is one of the important application technologies in aerospace field in the future. It will change the way of rocket launching and even be applied to aerospace kinetic energy weapon system. Aiming at the characteristics of various bottom indicators, uncertain information probability and expert participation in the effectiveness evaluation system of aircraft electromagnetic ejection system, based on the index hierarchy model, the method of RIMER(rule-base inference methodology using the evidential reasoning) is used to evaluate the effectiveness, which provides some reference for the design and development of aircraft electromagnetic launching system.

Applying Belief Rule-Based Inference Methodology Using Evidential Reasoning Approach to Clinical Reporting System

In the healthcare field, a critical issue is how to reason and represent uncertainties that present in clinical knowledge domain, signs, and symptoms to make the correct decisions. Although several researchers have developed various models of clinical modeling, many of them are incapable of handling uncertainties correctly. The paper provides the working details of rule-based inference methodology using evidence reasoning (RIMER) methodology applied to model the inference process and clinical guidelines. In RIMER, belief-degree are embedded in all possible consequences of the rule. It can handle uncertainties and provide a causal relationship between the rules. Traditional IF-THEN rules do not provide a causal relationship between antecedent and consequent attributes of the rule only provide that the rule is either 100% true or 100% false. Also, a case study is used to demonstrate that the results generated by the system using RIMER are more reliable in terms of accuracy and performance compared to results generated manually for a diabetes diagnosis.

RIMER and SA based thermal efficiency optimization for fired heaters

Due to frequent changes in thermal load and drift of online oxygen analyzer, the heater’s thermal efficiency optimization system with limited maintenance resources seldom works in long term. To solve this problem, a novel and practical optimization method combing RIMER (i.e. belief rule-base inference methodology using the evidential reasoning) approach and SA (stochastic approximation) on-line self-optimization is proposed. The optimization scheme consists of (i) an off-line expert system that determines the optimal steady state operation for a given thermal load, and (ii) an on-line optimization system that further improves the thermal efficiency to alleviate the influence caused by sensors’ drift. In more details, at the off-line stage, a belief-rule-base (BRB) expert system is constructed to determine good initial references of operating conditions for a specified thermal load, which quickly drives the system to a near optimal operation point when confronted with the thermal load change; this is based on RIMER. During on-line operation, these off-line determined initial values are further refined by using the SA approach to optimize the thermal efficiency. The newly obtained optimal operating condition then is updated online to compensate the sensor’s drift. The optimized profile is implemented through a practical control strategy for the flue gas – air system of fired heaters, which is applied to the flue gas oxygen concentration and chamber negative pressure control on the basis of flue gas-air control system. Simulation results on the UniSim™ Design platform demonstrate the feasibility of the proposed optimization scheme. Furthermore, the field implementation results at a real process illustrate the effectiveness of this optimization system. Both simulation and field application show that the thermal efficiency can be nearly improved by c. 1%.

Optimizing the configuration of an heterogeneous architecture of sensors for activity recognition, using the extended belief rule-based inference methodology

Smart environments are heterogeneous architectures with a broad range of heterogeneous electronic devices that are with high in processing capabilities for computing, considering low power consumption. They have the ability to record information about the behavior of the people by means of their interactions with the objects within an environment. This kind of environments are providing solutions to address some of the problems associated with the growing size and ageing of the population by means of the recognition of activities that can offer monitoring activities of daily living and adapting the environment. In order to deploy low-cost smart environments and reduce the computational complexity for activity recognition, it is a key issue to know the subset of sensors which are relevant for activity recognition. By using feature selection methods to optimize the subset of initial sensors in a smart environment, this paper proposes the adaption of the extended belief rule-based inference methodology (RIMER+) to handle data binary sensors and its use as the suitable classifier for activity recognition that keeps the accuracy of results even in situations where an essential sensor fails. A case study is presented in which a smart environment dataset for activity recognition with 14 sensors is set. Two optimizations with 7 and 10 sensors are obtained with two feature selection methods in which the adaptation of RIMER+ for smart environment provides an encouraged performance against the most popular classifiers in terms of robustness.

Dynamic rule adjustment approach for optimizing belief rule-base expert system

The belief rule-base (BRB) inference methodology, which uses the evidential reasoning (RIMER) approach, has been widely popular in recent years. As an expert-system methodology using the RIMER approach, BRB is used for storing various types of uncertain knowledge in the form of belief structure. Several structure-learning approaches have been proposed in recent years. However, these approaches are deficient in various aspects, do not have repeatability, hold incomplete data, and are constrained by the associated scale-utility value. Moreover, considering the influence of the number of rules for a BRB system, two scenarios are designed to reveal the relationship between structure feature and fewer/excessive rules. Excessive rules may lead to a BRB that is equipped with an over-complete structure, whereas significantly fewer rules may result in a BRB with an incomplete structure. To solve these problems, we initially proposed to develop an adjusted structure that is leading to the establishment of a complete structure instead of incomplete and over-complete structures. By scenario analysis and experimental verification through parameter learning of BRBs, we summarize several features of two scenarios, which can be used to reveal certain number of key BRB properties. Finally, density and error analyses are introduced to dynamically prune or add rules to construct the complete structure, particularly that of the BRB comprising multiple-antecedent attributes. We verify the effectiveness of the proposed approach by testing its use in a practical case study on oil pipeline-leak detection and demonstrate how the approach can be implemented.

A new belief rule base knowledge representation scheme and inference methodology using the evidential reasoning rule for evidence combination

In this paper, we extend the original belief rule-base inference methodology using the evidential reasoning approach by i) introducing generalised belief rules as knowledge representation scheme, and ii) using the evidential reasoning rule for evidence combination in the rule-base inference methodology instead of the evidential reasoning approach. The result is a new rule-base inference methodology which is able to handle a combination of various types of uncertainty.Generalised belief rules are an extension of traditional rules where each consequent of a generalised belief rule is a belief distribution defined on the power set of propositions, or possible outcomes, that are assumed to be collectively exhaustive and mutually exclusive. This novel extension allows any combination of certain, uncertain, interval, partial or incomplete judgements to be represented as rule-based knowledge. It is shown that traditional IF-THEN rules, probabilistic IF-THEN rules, and interval rules are all special cases of the new generalised belief rules.The rule-base inference methodology has been updated to enable inference within generalised belief rule bases. The evidential reasoning rule for evidence combination is used for the aggregation of belief distributions of rule consequents.

Belief rule-based inference for predicting trauma outcome

A belief rule-based inference methodology using the evidential reasoning approach (RIMER) is employed in this study to construct a decision support tool that helps physicians predict in-hospital death and intensive care unit admission among trauma patients in emergency departments (EDs). This study contributes to the research community by developing and validating a RIMER-based decision tool for predicting trauma outcome. To compare the prediction performance of the RIMER model with those of models derived using commonly adopted methods, such as logistic regression analysis, support vector machine (SVM), and artificial neural network (ANN), several logistic regression models, SVM models, and ANN models are constructed using the same dataset. Five-fold cross-validation is employed to train and validate the prediction models constructed using four different methods. Results indicate that the RIMER model has the best prediction performance among the four models, and its performance can be improved after knowledge base training with historical data. The RIMER tool exhibits strong potential to help ED physicians to better triage trauma, optimally utilize hospital resources, and achieve better patient outcomes.

Use of fuzzy risk assessment in FMEA of offshore engineering systems

This paper proposes a novel framework for analysing and synthesising engineering system risks on the basis of a generic Fuzzy Evidential Reasoning (FER) approach. The approach is developed to simplify the inference process and overcome the problems of traditional fuzzy rule based methods in risk analysis and decision making. The framework, together with the FER approach has been applied to model the safety of an offshore engineering system. The benchmarking between the new model and a well-established Rule based Inference Methodology using the Evidential Reasoning (RIMER) is conducted to demonstrate its reliability and unique characteristics. It will facilitate subjective risk assessment in different engineering systems where historical failure data is not available in their safety practice.

Sensor-based activity recognition using extended belief rule-based inference methodology.

The recently developed extended belief rule-based inference methodology (RIMER+) recognizes the need of modeling different types of information and uncertainty that usually coexist in real environments. A home setting with sensors located in different rooms and on different appliances can be considered as a particularly relevant example of such an environment, which brings a range of challenges for sensor-based activity recognition. Although RIMER+ has been designed as a generic decision model that could be applied in a wide range of situations, this paper discusses how this methodology can be adapted to recognize human activities using binary sensors within smart environments. The evaluation of RIMER+ against other state-of-the-art classifiers in terms of accuracy, efficiency and applicability was found to be significantly relevant, specially in situations of input data incompleteness, and it demonstrates the potential of this methodology and underpins the basis to develop further research on the topic.

A novel belief rule base representation, generation and its inference methodology

Advancement and application of rule-based systems have always been a key research area in computer-aided support for human decision making due to the fact that rule base is one of the most common frameworks for expressing various types of human knowledge in an intelligent system. In this paper, a novel rule-based representation scheme with a belief structure is proposed firstly along with its inference methodology. Such a rule base is designed with belief degrees embedded in the consequent terms as well as in the all antecedent terms of each rule, which is shown to be capable of capturing vagueness, incompleteness, uncertainty, and nonlinear causal relationships in an integrated way. The overall representation and inference framework offers a further improvement and great extension of the recently developed belief Rule base Inference Methodology (refer to as RIMER), although they still share a common scheme at the final step of inference, i.e., the evidential reasoning (ER) approach is applied to the rule combination. It is worth noting that this new extended belief rule base representation is a great extension of traditional rule base as well as fuzzy rule base by encompassing the uncertainty description in the rule antecedent and consequent. Subsequently, a simple but efficient and powerful method for automatically generating such extended belief rule base from numerical data is proposed involving neither time-consuming iterative learning procedure nor complicated rule generation mechanisms but keeping the relatively good performance, which thanks to the new features of the extended rule base with belief structures. Then some case studies in oil pipeline leak detection and software defect detection are provided to illustrate the proposed new rule base representation, generation, and inference procedure as well as demonstrate its high performance and efficiency by comparing with some existing approaches.

Application of Three-Ratio Fault Diagnosis Rule Based on Belief Rule Base

A transformer fault diagnosis method based on RIMER( belief rule-base inference methodology using the evidential reasoning approach) expert system is proposed to deal with fault coding loss and low accuracy in the three-ratio rule. The fault diagnosis method acquires the parameter of the belief rule bases from both three-ratio rule and training of the fault sample data, and then get the diagnosis result Based on the belief rule bases and evidence reasoning algorithm. The calculation speed of the method is fast. The simulation experiment indicates that the model improves the fault diagnosis accuracy rate, furthermore the distributed confidence result indicts the mixed fault more effective.

Structure learning for belief rule base expert system: A comparative study

The Belief Rule Base (BRB) is an expert system which can handle both qualitative and quantitative information. One of the applications of the BRB is the Rule-base Inference Methodology using the Evidential Reasoning approach (RIMER). Using the BRB, RIMER can handle different types of information under uncertainty. However, there is a combinatorial explosion problem when there are too many attributes and/or too many alternatives for each attribute in the BRB. Most current approaches are designed to reduce the number of the alternatives for each attribute, where the rules are derived from physical systems and redundant in numbers. However, these approaches are not applicable when the rules are given by experts and the BRB should not be oversized. A structure learning approach is proposed using Grey Target (GT), Multidimensional Scaling (MDS), Isomap and Principle Component Analysis (PCA) respectively, named as GT–RIMER, MDS–RIMER, Isomap–RIMER and PCA–RIMER. A case is studied to evaluate the overall capability of an Armored System of Systems. The efficiency of the proposed approach is validated by the case study results: the BRB is downsized using any of the four techniques, and PCA–RIMER has shown excellent performance. Furthermore, the robustness of PCA–RIMER is further verified under different conditions with varied number of attributes.

Iterative learning belief rule-base inference methodology using evidential reasoning for delayed coking unit

The belief rule-base inference methodology using evidential reasoning (RIMER) approach has been proved to be an effective extension of traditional rule-based expert systems and a powerful tool for representing more complicated causal relationships using different types of information with uncertainties. With a predetermined structure of the initial belief rule-base (BRB), the RIMER approach requires the assignment of some system parameters including rule weights, attribute weights, and belief degrees using experts’ knowledge. Although some updating algorithms were proposed to solve this problem, it is still difficult to find an optimal compact BRB. In this paper, a novel updating algorithm is proposed based on iterative learning strategy for delayed coking unit (DCU), which contains both continuous and discrete characteristics. Daily DCU operations under different conditions are modeled by a BRB, which is then updated using iterative learning methodology, based on a novel statistical utility for every belief rule. Compared with the other learning algorithms, our methodology can lead to a more optimal compact final BRB. With the help of this expert system, a feedforward compensation strategy is introduced to eliminate the disturbance caused by the drum-switching operations. The advantages of this approach are demonstrated on the UniSim™ Operations Suite platform through the developed DCU operation expert system modeled and optimized from a real oil refinery.

A belief rule-based decision support system for clinical risk assessment of cardiac chest pain

This paper describes a prototype clinical decision support system (CDSS) for risk stratification of patients with cardiac chest pain. A newly developed belief rule-based inference methodology-RIMER was employed for developing the prototype. Based on the belief rule-based inference methodology, the prototype CDSS can deal with uncertainties in both clinical domain knowledge and clinical data. Moreover, the prototype can automatically update its knowledge base via a belief rule base (BRB) learning module which can adjust BRB through accumulated historical clinical cases. The domain specific knowledge used to construct the knowledge base of the prototype was learned from real patient data. We simulated a set of 1000 patients in cardiac chest pain to validate the prototype. The belief rule-based prototype CDSS has been found to perform extremely well. Firstly, the system can provide more reliable and informative diagnosis recommendations than manual diagnosis using traditional rules when there are clinical uncertainties. Secondly, the diagnostic performance of the system can be significantly improved after training the BRB through accumulated clinical cases.

Online Updating Belief-Rule-Base Using the RIMER Approach

In order to determine the parameters of belief-rule-base (BRB) accurately, several optimization methods have been proposed for training BRB, on the basis of a generic rule-base inference methodology using the evidential reasoning (RIMER) approach. These optimization methods are implemented offline, and such are not suitable for training BRB in a dynamic fashion. In this paper, two recursive algorithms are proposed to update BRB online that can simulate dynamic systems. The main feature of the proposed algorithms is that only partial input and output information is required, which can be incomplete or vague, numerical or judgmental, or mixed. If the internal structure of a BRB is initially decided using expert judgments, domain-specific knowledge and/or commonsense rules, the proposed algorithms can be used to fine-tune the initial BRB online, once input and output datasets become available. Using the proposed algorithms, there is no need to collect a complete set of data before a BRB can be trained, which is necessary if the BRB is used to simulate a dynamic system. A numerical example and a case study are reported to demonstrate the potential of the algorithms for online fault diagnosis.

A methodology to generate a belief rule base for customer perception risk analysis in new product development

New product development (NPD) is crucial for a company’s success in a competitive market. Meanwhile, NPD is a process associated with great complexity and high risk. To ensure its smooth operation, risks involved in an NPD process need to be analyzed in a proper way. In this paper, a novel method is proposed to generate a belief rule base (BRB), which is the basis of the Belief Rule-Base Inference Methodology using the Evidential Reasoning (RIMER). Due to its capability in dealing with complex reasoning problems under uncertainty, RIMER is then applied to assess customer perception risk (CPR) in an NPD process. To test and validate the method proposed in this paper, a case study of an “Interactive Doll” is conducted at the end of the paper.

The polychronic economy

The polychronic economy